Paper Digest: Recent Papers on Generative Adversarial Network
Paper Digest Team extracted all recent Generative Adversarial Network (GAN) related papers on our radar, and generated highlight sentences for them. The results are then sorted by relevance & date. In addition to this ‘static’ page, we also provide a real-time version of this article, which has more coverage and is updated in real time to include the most recent updates on this topic. Based in New York, Paper Digest is dedicated to producing high-quality text analysis results that people can acturally use on a daily basis. Since 2018, we have been serving users across the world with a number of exclusive services on ranking, search, tracking and automatic literature review.
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TABLE 1: Paper Digest: Recent Papers on Generative Adversarial Network
| Paper | Author(s) | Source | Date | |
|---|---|---|---|---|
| 1 | Increasing Confidence in Adversarial Robustness Evaluations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a test to identify weak attacks, and thus weak defense evaluations. |
Roland S. Zimmermann; Wieland Brendel; Florian Tramer; Nicholas Carlini; | arxiv-cs.LG | 2022-06-28 |
| 2 | Sound Model Factory: An Integrated System Architecture for Generative Audio Modelling Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new system for data-driven audio sound model design built around two different neural network architectures, a Generative Adversarial Network(GAN) and a Recurrent Neural Network (RNN), that takes advantage of the unique characteristics of each to achieve the system objectives that neither is capable of addressing alone. |
Lonce Wyse; Purnima Kamath; Chitralekha Gupta; | arxiv-cs.SD | 2022-06-27 |
| 3 | Defense Against Adversarial Attacks on Deep Convolutional Neural Networks Through Nonlocal Denoising Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Despite substantial advances in network architecture performance, the susceptibility of adversarial attacks makes deep learning challenging to implement in safety-critical applications. This paper proposes a data-centric approach to addressing this problem. |
Sandhya Aneja; Nagender Aneja; Pg Emeroylariffion Abas; Abdul Ghani Naim; | arxiv-cs.CV | 2022-06-25 |
| 4 | Adversarial Robustness of Deep Neural Networks: A Survey from A Formal Verification Perspective Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we survey existing literature in adversarial robustness verification for neural networks and collect 39 diversified research works across machine learning, security, and software engineering domains. |
MARK HUASONG MENG et. al. | arxiv-cs.CR | 2022-06-24 |
| 5 | Learning Agile Skills Via Adversarial Imitation of Rough Partial Demonstrations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a generative adversarial method for inferring reward functions from partial and potentially physically incompatible demonstrations for successful skill acquirement where reference or expert demonstrations are not easily accessible. |
CHENHAO LI et. al. | arxiv-cs.RO | 2022-06-23 |
| 6 | LED: Latent Variable-based Estimation of Density Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we propose LED, a new generative model closely related to GANs, that allows not only efficient sampling but also efficient density estimation. |
Omri Ben-Dov; Pravir Singh Gupta; Victoria Fernandez Abrevaya; Michael J. Black; Partha Ghosh; | arxiv-cs.LG | 2022-06-23 |
| 7 | Latent Policies for Adversarial Imitation Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: A key insight of this work is that performing imitation learning in a suitable latent task space makes the training process stable, even in challenging high-dimensional problems. |
Tianyu Wang; Nikhil Karnwal; Nikolay Atanasov; | arxiv-cs.LG | 2022-06-22 |
| 8 | Shilling Black-box Recommender Systems By Learning to Generate Fake User Profiles Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study Shilling Attack where an adversarial party injects a number of fake user profiles for improper purposes. |
CHEN LIN et. al. | arxiv-cs.IR | 2022-06-22 |
| 9 | A Study on The Evaluation of Generative Models Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study the evaluation metrics of generative models by generating a high-quality synthetic dataset on which we can estimate classical metrics for comparison. |
Eyal Betzalel; Coby Penso; Aviv Navon; Ethan Fetaya; | arxiv-cs.LG | 2022-06-22 |
| 10 | Temporally Consistent Semantic Video Editing Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a simple yet effective method to facilitate temporally coherent video editing. |
Yiran Xu; Badour AlBahar; Jia-Bin Huang; | arxiv-cs.CV | 2022-06-21 |
| 11 | Using EBGAN for Anomaly Intrusion Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we put forward an EBGAN-based intrusion detection method, IDS-EBGAN, that classifies network records as normal traffic or malicious traffic. |
YI CUI et. al. | arxiv-cs.CR | 2022-06-21 |
| 12 | Convex Space Learning Improves Deep-generative Oversampling for Tabular Imbalanced Classification on Smaller Datasets Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this article, we show that existing deep generative models perform poorly compared to linear interpolation approaches generating synthetic samples from the convex space of the minority class, for imbalanced classification problems on tabular datasets of small size. |
KRISTIAN SCHULTZ et. al. | arxiv-cs.LG | 2022-06-20 |
| 13 | StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the taxonomy of GAN approaches and present a new open-source library named StudioGAN. |
Minguk Kang; Joonghyuk Shin; Jaesik Park; | arxiv-cs.CV | 2022-06-19 |
| 14 | Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we will inform the GAN based predictions using stochastic traffic flow models and develop a GAN based PIDL framework for TSE, named “PhysGAN-TSE. |
Zhaobin Mo; Yongjie Fu; Xuan Di; | arxiv-cs.LG | 2022-06-19 |
| 15 | TrafficFlowGAN: Physics-informed Flow Based Generative Adversarial Network for Uncertainty Quantification Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes the TrafficFlowGAN, a physics-informed flow based generative adversarial network (GAN), for uncertainty quantification (UQ) of dynamical systems. |
Zhaobin Mo; Yongjie Fu; Daran Xu; Xuan Di; | arxiv-cs.LG | 2022-06-18 |
| 16 | On The Role of Generalization in Transferability of Adversarial Examples Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to demonstrate the role of the generalization properties of the substitute classifier used for generating adversarial examples in the transferability of the attack scheme to unobserved NN classifiers. |
Yilin Wang; Farzan Farnia; | arxiv-cs.LG | 2022-06-18 |
| 17 | Landscape Learning for Neural Network Inversion Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a method that learns a loss landscape where gradient descent is efficient, bringing massive improvement and acceleration to the inversion process. |
Ruoshi Liu; Chengzhi Mao; Purva Tendulkar; Hao Wang; Carl Vondrick; | arxiv-cs.CV | 2022-06-17 |
| 18 | Texture Generation Using Graph Generative Adversarial Network And Differentiable Rendering Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We thus present a new system called a graph generative adversarial network (GGAN) that can generate textures which can be directly integrated into a given 3D mesh models with tools like Blender and Unreal Engine and can be simulated from any perspective and lighting condition easily. |
Dharma KC; Clayton T. Morrison; Bradley Walls; | arxiv-cs.CV | 2022-06-17 |
| 19 | SOS: Score-based Oversampling for Tabular Data Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: SGMs are known to surpass other generative models, e.g., generative adversarial networks (GANs) and variational autoencoders (VAEs). Being inspired by their big success, in this work, we fully customize them for generating fake tabular data. |
JAYOUNG KIM et. al. | arxiv-cs.LG | 2022-06-17 |
| 20 | Comment on Transferability and Input Transformation with Additive Noise Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we analyze the relationship between transferability and input transformation with additive noise by mathematically proving that the modified optimization can produce more transferable adversarial examples. |
Hoki Kim; Jinseong Park; Jaewook Lee; | arxiv-cs.LG | 2022-06-17 |
| 21 | Understanding Robust Overfitting of Adversarial Training and Beyond Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Given these observations, we further designed data ablation adversarial training and identify that some small-loss data which are not worthy of the adversary strength cause robust overfitting in the strong adversary mode. To relieve this issue, we propose \emph{minimum loss constrained adversarial training} (MLCAT): in a minibatch, we learn large-loss data as usual, and adopt additional measures to increase the loss of the small-loss data. |
CHAOJIAN YU et. al. | arxiv-cs.LG | 2022-06-17 |
| 22 | Analysis and Extensions of Adversarial Training for Video Classification Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our second contribution is to show that using a smaller (sub-optimal) attack budget at training time leads to a more robust performance at test time. Based on these findings, we propose three defenses against attacks with variable attack budgets. |
Kaleab A. Kinfu; René Vidal; | arxiv-cs.CV | 2022-06-16 |
| 23 | Boosting The Adversarial Transferability of Surrogate Model with Dark Knowledge Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a method of training a surrogate model with abundant dark knowledge to boost the adversarial transferability of the adversarial examples generated by the surrogate model. |
Dingcheng Yang; Zihao Xiao; Wenjian Yu; | arxiv-cs.LG | 2022-06-16 |
| 24 | Adversarial Privacy Protection on Speech Enhancement Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an adversarial method to degrade speech enhancement systems. |
Mingyu Dong; Diqun Yan; Rangding Wang; | arxiv-cs.SD | 2022-06-16 |
| 25 | Gradient-Based Adversarial and Out-of-Distribution Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to utilize gradients for detecting adversarial and out-of-distribution samples. |
Jinsol Lee; Mohit Prabhushankar; Ghassan AlRegib; | arxiv-cs.LG | 2022-06-16 |
| 26 | Physics-Infused Fuzzy Generative Adversarial Network for Robust Failure Prognosis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a novel hybrid modeling approach for prognostics applications based on combining concepts from fuzzy logic and generative adversarial networks (GANs) is outlined. |
Ryan Nguyen; Shubhendu Kumar Singh; Rahul Rai; | arxiv-cs.AI | 2022-06-15 |
| 27 | Hardening DNNs Against Transfer Attacks During Network Compression Using Greedy Adversarial Pruning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate the adversarial robustness of models produced by several irregular pruning schemes and by 8-bit quantization. |
Jonah O’Brien Weiss; Tiago Alves; Sandip Kundu; | arxiv-cs.LG | 2022-06-15 |
| 28 | Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases. |
RONGGUANG WANG et. al. | arxiv-cs.LG | 2022-06-14 |
| 29 | Human Eyes Inspired Recurrent Neural Networks Are More Robust Against Adversarial Noises Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the brain, we design recurrent neural networks, including an input sampler that mimics the human retina, a dorsal network that guides where to look next, and a ventral network that represents the retinal samples. Taking these modules together, the models learn to take multiple glances at an image, attend to a salient part at each glance, and accumulate the representation over time to recognize the image. |
Minkyu Choi; Yizhen Zhang; Kuan Han; Xiaokai Wang; Zhongming Liu; | arxiv-cs.CV | 2022-06-14 |
| 30 | Defending Observation Attacks in Deep Reinforcement Learning Via Detection and Denoising Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider attacks manifesting as perturbations in the observation space managed by the external environment. |
Zikang Xiong; Joe Eappen; He Zhu; Suresh Jagannathan; | arxiv-cs.LG | 2022-06-14 |
| 31 | Downlink Power Allocation in Massive MIMO Via Deep Learning: Adversarial Attacks and Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The aim of this paper is twofold: (i) we consider a regression problem in a wireless setting and show that adversarial attacks can break the DL-based approach and (ii) we analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly. |
B. R. Manoj; Meysam Sadeghi; Erik G. Larsson; | arxiv-cs.LG | 2022-06-14 |
| 32 | Exploring Adversarial Attacks and Defenses in Vision Transformers Trained with DINO Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work conducts the first analysis on the robustness against adversarial attacks on self-supervised Vision Transformers trained using DINO. |
Javier Rando; Nasib Naimi; Thomas Baumann; Max Mathys; | arxiv-cs.CV | 2022-06-14 |
| 33 | Adversarial Vulnerability of Randomized Ensembles Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, this impressive performance raises the question: Are these robustness gains provided by randomized ensembles real? In this work we address this question both theoretically and empirically. |
Hassan Dbouk; Naresh R. Shanbhag; | arxiv-cs.LG | 2022-06-14 |
| 34 | When Adversarial Attacks Become Interpretable Counterfactual Explanations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We argue that, when learning a 1-Lipschitz neural network with the dual loss of an optimal transportation problem, the gradient of the model is both the direction of the transportation plan and the direction to the closest adversarial attack. |
Mathieu Serrurier; Franck Mamalet; Thomas Fel; Louis Béthune; Thibaut Boissin; | arxiv-cs.AI | 2022-06-14 |
| 35 | Distributed Adversarial Training to Robustify Deep Neural Networks at Scale Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Spurred by that, we propose distributed adversarial training (DAT), a large-batch adversarial training framework implemented over multiple machines. |
GAOYUAN ZHANG et. al. | arxiv-cs.LG | 2022-06-13 |
| 36 | Security of Machine Learning-Based Anomaly Detection in Cyber Physical Systems Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we focus on the impact of adversarial attacks on deep learning-based anomaly detection in CPS networks and implement a mitigation approach against the attack by retraining models using adversarial samples. |
ZAHRA JADIDI et. al. | arxiv-cs.DC | 2022-06-12 |
| 37 | Early Transferability of Adversarial Examples in Deep Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: What we discovered was that these two adversarial directions started to align with each other already after the first few training steps (which typically use only a small fraction of the available training data), even though the accuracy of the two networks hadn’t started to improve from their initial bad values due to the early stage of the training. The purpose of this paper is to present this phenomenon experimentally and propose plausible explanations for some of its properties. |
Oriel BenShmuel; | arxiv-cs.LG | 2022-06-09 |
| 38 | ReFace: Real-time Adversarial Attacks on Face Recognition Systems Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose ReFace, a real-time, highly-transferable attack on face recognition models based on Adversarial Transformation Networks (ATNs). |
SHEHZEEN HUSSAIN et. al. | arxiv-cs.CV | 2022-06-09 |
| 39 | Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Adversarial examples, which are usually generated for specific inputs with a specific model, are ubiquitous for neural networks. In this paper we unveil a surprising property of adversarial noises when they are put together, i.e., adversarial noises crafted by one-step gradient methods are linearly separable if equipped with the corresponding labels. |
Huishuai Zhang; Da Yu; Yiping Lu; Di He; | arxiv-cs.LG | 2022-06-09 |
| 40 | CARLA-GeAR: A Dataset Generator for A Systematic Evaluation of Adversarial Robustness of Vision Models Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: All the code and datasets used in this paper are available at http://carlagear.retis.santannapisa.it. |
Federico Nesti; Giulio Rossolini; Gianluca D’Amico; Alessandro Biondi; Giorgio Buttazzo; | arxiv-cs.CV | 2022-06-09 |
| 41 | GAN I Hire You? — A System for Personalized Virtual Job Interview Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: A technological approach for generating such feedback might be a playful and low-key starting point for job interview training. Therefore, we extended an interactive virtual job interview training system with a Generative Adversarial Network (GAN)-based approach that first detects behavioral weaknesses and subsequently generates personalized feedback. |
ALEXANDER HEIMERL et. al. | arxiv-cs.HC | 2022-06-08 |
| 42 | Wavelet Regularization Benefits Adversarial Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a wavelet regularization method based on the Haar wavelet decomposition which is named Wavelet Average Pooling. |
JUN YAN et. al. | arxiv-cs.CV | 2022-06-08 |
| 43 | Latent Boundary-guided Adversarial Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To achieve a better trade-off between standard accuracy and adversarial robustness, we propose a novel adversarial training framework called LAtent bounDary-guided aDvErsarial tRaining (LADDER) that adversarially trains DNN models on latent boundary-guided adversarial examples. |
Xiaowei Zhou; Ivor W. Tsang; Jie Yin; | arxiv-cs.LG | 2022-06-08 |
| 44 | GCFSR: A Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a generative and controllable face SR framework, called GCFSR, which can reconstruct images with faithful identity information without any additional priors. |
Jingwen He; Wu Shi; Kai Chen; Lean Fu; Chao Dong; | cvpr | 2022-06-07 |
| 45 | Auditing Privacy Defenses in Federated Learning Via Generative Gradient Leakage Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we validate that the private training data can still be leaked under certain defense settings with a new type of leakage, i.e., Generative Gradient Leakage (GGL). |
Zhuohang Li; Jiaxin Zhang; Luyang Liu; Jian Liu; | cvpr | 2022-06-07 |
| 46 | DO-GAN: A Double Oracle Framework for Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new approach to train Generative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discriminator oracles. |
AYE PHYU PHYU AUNG et. al. | cvpr | 2022-06-07 |
| 47 | Feature Statistics Mixing Regularization for Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: As a remedy, we propose feature statistics mixing regularization (FSMR) that encourages the discriminator’s prediction to be invariant to the styles of input images. |
Junho Kim; Yunjey Choi; Youngjung Uh; | cvpr | 2022-06-07 |
| 48 | BigDatasetGAN: Synthesizing ImageNet With Pixel-Wise Annotations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we scale DatasetGAN to ImageNet scale of class diversity. |
DAIQING LI et. al. | cvpr | 2022-06-07 |
| 49 | Polymorphic-GAN: Generating Aligned Samples Across Multiple Domains With Learned Morph Maps Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a generative adversarial network that can simultaneously generate aligned image samples from multiple related domains. |
Seung Wook Kim; Karsten Kreis; Daiqing Li; Antonio Torralba; Sanja Fidler; | cvpr | 2022-06-07 |
| 50 | Cycle-Consistent Counterfactuals By Latent Transformations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel approach, Cycle-Consistent Counterfactuals by Latent Transformations (C3LT), which learns a latent transformation that automatically generates visual CFs by steering in the latent space of generative models. |
Saeed Khorram; Li Fuxin; | cvpr | 2022-06-07 |
| 51 | Learning To Restore 3D Face From In-the-Wild Degraded Images Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In-the-wild 3D face modelling is a challenging problem as the predicted facial geometry and texture suffer from a lack of reliable clues or priors, when the input images are degraded. To address such a problem, in this paper we propose a novel Learning to Restore (L2R) 3D face framework for unsupervised high-quality face reconstruction from low-resolution images. |
ZHENYU ZHANG et. al. | cvpr | 2022-06-07 |
| 52 | Towards Efficient Data Free Black-Box Adversarial Attack Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, by rethinking the collaborative relationship between the generator and the substitute model, we design a novel black-box attack framework. |
JIE ZHANG et. al. | cvpr | 2022-06-07 |
| 53 | SphericGAN: Semi-Supervised Hyper-Spherical Generative Adversarial Networks for Fine-Grained Image Synthesis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To reduce the dependence of generative models on labeled data, we propose a semi-supervised hyper-spherical GAN for class-conditional fine-grained image generation, and our model is referred to as SphericGAN. |
TIANYI CHEN et. al. | cvpr | 2022-06-07 |
| 54 | DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To these ends, we propose a simpler but more effective Deep Fusion Generative Adversarial Networks (DF-GAN). |
MING TAO et. al. | cvpr | 2022-06-07 |
| 55 | Improving GAN Equilibrium By Raising Spatial Awareness Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate the issue of D dominating the competition in GANs, we aim to raise the spatial awareness of G. Randomly sampled multi-level heatmaps are encoded into the intermediate layers of G as an inductive bias. |
JIANYUAN WANG et. al. | cvpr | 2022-06-07 |
| 56 | Self-Supervised Dense Consistency Regularization for Image-to-Image Translation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a simple but effective regularization technique for improving GAN-based image-to-image translation. |
MINSU KO et. al. | cvpr | 2022-06-07 |
| 57 | Depth-Aware Generative Adversarial Network for Talking Head Video Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a self-supervised face-depth learning method to automatically recover dense 3D facial geometry (i.e. depth) from the face videos without the requirement of any expensive 3D annotation data. |
Fa-Ting Hong; Longhao Zhang; Li Shen; Dan Xu; | cvpr | 2022-06-07 |
| 58 | Few Shot Generative Model Adaption Via Relaxed Spatial Structural Alignment Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, existing methods are prone to model overfitting and collapse in extremely few shot setting (less than 10). To solve this problem, we propose a relaxed spatial structural alignment (RSSA) method to calibrate the target generative models during the adaption. |
Jiayu Xiao; Liang Li; Chaofei Wang; Zheng-Jun Zha; Qingming Huang; | cvpr | 2022-06-07 |
| 59 | Multi-View Consistent Generative Adversarial Networks for 3D-Aware Image Synthesis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, one key challenge remains: existing approaches lack geometry constraints, hence usually fail to generate multi-view consistent images. To address this challenge, we propose Multi-View Consistent Generative Adversarial Networks (MVCGAN) for high-quality 3D-aware image synthesis with geometry constraints. |
XUANMENG ZHANG et. al. | cvpr | 2022-06-07 |
| 60 | Exemplar-Based Pattern Synthesis With Implicit Periodic Field Network Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an exemplar-based visual pattern synthesis framework that aims to model the inner statistics of visual patterns and generate new, versatile patterns that meet the aforementioned requirements. |
Haiwei Chen; Jiayi Liu; Weikai Chen; Shichen Liu; Yajie Zhao; | cvpr | 2022-06-07 |
| 61 | Protecting Facial Privacy: Generating Adversarial Identity Masks Via Style-Robust Makeup Transfer Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose adversarial makeup transfer GAN (AMT-GAN), a novel face protection method aiming at constructing adversarial face images that preserve stronger black-box transferability and better visual quality simultaneously. |
SHENGSHAN HU et. al. | cvpr | 2022-06-07 |
| 62 | LAR-SR: A Local Autoregressive Model for Image Super-Resolution Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the fact that given the structural information, the textural details in the natural images are locally related without long term dependency, in this paper we propose a novel autoregressive model-based SR approach, namely LAR-SR, which can efficiently generate realistic SR images using a novel local autoregressive (LAR) module. |
BAISONG GUO et. al. | cvpr | 2022-06-07 |
| 63 | DetectorDetective: Investigating The Effects of Adversarial Examples on Object Detectors Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose DetectorDetective, an interactive visual tool that aims to help users better understand the behaviors of a model as adversarial images journey through an object detector. |
SIVAPRIYA VELLAICHAMY et. al. | cvpr | 2022-06-07 |
| 64 | Adversarial Reprogramming Revisited Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Adversarial reprogramming, introduced by Elsayed, Goodfellow, and Sohl-Dickstein, seeks to repurpose a neural network to perform a different task, by manipulating its input without modifying its weights. We prove that two-layer ReLU neural networks with random weights can be adversarially reprogrammed to achieve arbitrarily high accuracy on Bernoulli data models over hypercube vertices, provided the network width is no greater than its input dimension. |
Matthias Englert; Ranko Lazic; | arxiv-cs.LG | 2022-06-07 |
| 65 | Adversarial Texture for Fooling Person Detectors in The Physical World Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a generative method, named Toroidal-Cropping-based Expandable Generative Attack (TC-EGA), to craft AdvTexture with repetitive structures. |
ZHANHAO HU et. al. | cvpr | 2022-06-07 |
| 66 | LAS-AT: Adversarial Training With Learnable Attack Strategy Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework for adversarial training by introducing the concept of "learnable attack strategy", dubbed LAS-AT, which learns to automatically produce attack strategies to improve the model robustness. |
XIAOJUN JIA et. al. | cvpr | 2022-06-07 |
| 67 | Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider the FSIC problem in the case of adversarial examples. |
Junhao Dong; Yuan Wang; Jian-Huang Lai; Xiaohua Xie; | cvpr | 2022-06-07 |
| 68 | Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To bridge adversarial robustness and model compression, we propose a novel adversarial pruning method, Masking Adversarial Damage (MAD) that employs second-order information of adversarial loss. |
Byung-Kwan Lee; Junho Kim; Yong Man Ro; | cvpr | 2022-06-07 |
| 69 | Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop a theoretical framework for adversarial training with FW optimization (FW-AT) that reveals a geometric connection between the loss landscape and the distortion of l-inf FW attacks (the attack’s l-2 norm). |
Theodoros Tsiligkaridis; Jay Roberts; | cvpr | 2022-06-07 |
| 70 | Scene Aware Person Image Generation Through Global Contextual Conditioning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel pipeline to generate and insert contextually relevant person images into an existing scene while preserving the global semantics. |
Prasun Roy; Subhankar Ghosh; Saumik Bhattacharya; Umapada Pal; Michael Blumenstein; | arxiv-cs.CV | 2022-06-06 |
| 71 | Winner Does Not Take All: Contrasting Centrality in Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a random graph model that generates directed graphs with low-key leaders. |
Anthony Bonato; Joey Kapusin; Jiajie Yuan; | arxiv-cs.SI | 2022-06-05 |
| 72 | Soft Adversarial Training Can Retain Natural Accuracy Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a training framework that can retain natural accuracy without sacrificing robustness in a constrained setting. |
Abhijith Sharma; Apurva Narayan; | arxiv-cs.LG | 2022-06-04 |
| 73 | D’ARTAGNAN: Counterfactual Video Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We combine deep neural networks, twin causal networks and generative adversarial methods for the first time to build D’ARTAGNAN (Deep ARtificial Twin-Architecture GeNerAtive Networks), a novel causal generative model. |
HADRIEN REYNAUD et. al. | arxiv-cs.CV | 2022-06-03 |
| 74 | Causality Learning With Wasserstein Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: It simultaneously learns causal structures while improving its data generation capability. We compare the performance of DAG-WGAN with other models that do not involve the Wasserstein metric in order to identify its contribution to causal structure learning. |
Hristo Petkov; Colin Hanley; Feng Dong; | arxiv-cs.LG | 2022-06-03 |
| 75 | An Unpooling Layer for Graph Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel and trainable graph unpooling layer for effective graph generation. |
Yinglong Guo; Dongmian Zou; Gilad Lerman; | arxiv-cs.LG | 2022-06-03 |
| 76 | Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an optimization model-informed generative adversarial network (MI-GAN) framework to solve OPF under uncertainty. |
Yuxuan Li; Chaoyue Zhao; Chenang Liu; | arxiv-cs.LG | 2022-06-03 |
| 77 | Adversarial Laser Spot: Robust and Covert Physical Adversarial Attack to DNNs Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The light-based physical adversarial attack technology has excellent covertness, which brings great security risks to many applications based on deep neural networks (such as automatic driving technology). Therefore, we propose a robust physical adversarial attack technology with excellent covertness, called adversarial laser point (AdvLS), which optimizes the physical parameters of laser point through genetic algorithm to perform physical adversarial attack. |
Chengyin Hu; | arxiv-cs.CV | 2022-06-02 |
| 78 | FACM: Correct The Output of Deep Neural Network with Middle Layers Features Against Adversarial Samples Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In the strong adversarial attacks against deep neural network (DNN), the output of DNN will be misclassified if and only if the last feature layer of the DNN is completely destroyed by adversarial samples, while our studies found that the middle feature layers of the DNN can still extract the effective features of the original normal category in these adversarial attacks. To this end, in this paper, a middle $\bold{F}$eature layer $\bold{A}$nalysis and $\bold{C}$onditional $\bold{M}$atching prediction distribution (FACM) model is proposed to increase the robustness of the DNN against adversarial samples through correcting the output of DNN with the features extracted by the middle layers of DNN. |
Xiangyuan Yang; Jie Lin; Hanlin Zhang; Xinyu Yang; Peng Zhao; | arxiv-cs.CV | 2022-06-02 |
| 79 | The Robust Way to Stack and Bag: The Local Lipschitz Way Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Recent research has established that the local Lipschitz constant of a neural network directly influences its adversarial robustness. |
Thulasi Tholeti; Sheetal Kalyani; | arxiv-cs.LG | 2022-06-01 |
| 80 | On The Reversibility of Adversarial Attacks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the predictability of the mapping between the classes predicted for original images and for their corresponding adversarial examples. |
Chau Yi Li; Ricardo Sánchez-Matilla; Ali Shahin Shamsabadi; Riccardo Mazzon; Andrea Cavallaro; | arxiv-cs.LG | 2022-06-01 |
| 81 | Generative Models with Information-Theoretic Protection Against Membership Inference Attacks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an information theoretically motivated regularization term that prevents the generative model from overfitting to training data and encourages generalizability. |
Parisa Hassanzadeh; Robert E. Tillman; | arxiv-cs.LG | 2022-05-31 |
| 82 | MontageGAN: Generation and Assembly of Multiple Components By GANs Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose MontageGAN, which is a Generative Adversarial Networks (GAN) framework for generating multi-layer images. |
Chean Fei Shee; Seiichi Uchida; | arxiv-cs.CV | 2022-05-31 |
| 83 | CALEB: A Conditional Adversarial Learning Framework to Enhance Bot Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work is motivated by the critical need to establish adaptive bot detection methods in order to proactively capture unseen evolved bots towards healthier OSNs interactions. In contrast with most earlier supervised ML approaches which are limited by the inability to effectively detect new types of bots, this paper proposes CALEB, a robust end-to-end proactive framework based on the Conditional Generative Adversarial Network (CGAN) and its extension, Auxiliary Classifier GAN (AC-GAN), to simulate bot evolution by creating realistic synthetic instances of different bot types. |
George Dialektakis; Ilias Dimitriadis; Athena Vakali; | arxiv-cs.SI | 2022-05-31 |
| 84 | Searching for The Essence of Adversarial Perturbations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we demonstrate that adversarial perturbations contain human-recognizable information, which is the key conspirator responsible for a neural network’s erroneous prediction. |
Dennis Y. Menn; Hung-yi Lee; | arxiv-cs.LG | 2022-05-30 |
| 85 | Why Adversarial Training of ReLU Networks Is Difficult? Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper mathematically derives an analytic solution of the adversarial perturbation on a ReLU network, and theoretically explains the difficulty of adversarial training. |
XU CHENG et. al. | arxiv-cs.LG | 2022-05-30 |
| 86 | Exposing Fine-grained Adversarial Vulnerability of Face Anti-spoofing Models Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: So we propose fine-grained attacks for exposing adversarial vulnerability of face anti-spoofing models. |
Songlin Yang; Wei Wang; Chenye Xu; Bo Peng; Jing Dong; | arxiv-cs.CV | 2022-05-30 |
| 87 | Robust Weight Perturbation for Adversarial Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: A criterion that regulates the weight perturbation is therefore crucial for adversarial training. In this paper, we propose such a criterion, namely Loss Stationary Condition (LSC) for constrained perturbation. |
CHAOJIAN YU et. al. | arxiv-cs.LG | 2022-05-29 |
| 88 | Enhancing Quality of Pose-varied Face Restoration with Local Weak Feature Sensing and GAN Prior Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a well-designed blind face restoration network with generative facial prior. |
KAI HU et. al. | arxiv-cs.CV | 2022-05-28 |
| 89 | Multiscale Voxel Based Decoding For Enhanced Natural Image Reconstruction From Brain Activity Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we present a novel approach for enhanced image reconstruction, in which existing methods for object decoding and image reconstruction are merged together. |
Mali Halac; Murat Isik; Hasan Ayaz; Anup Das; | arxiv-cs.CV | 2022-05-27 |
| 90 | DH-GAN: A Physics-driven Untrained Generative Adversarial Network for 3D Microscopic Imaging Using Digital Holography Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a new DL architecture based on generative adversarial networks that uses a discriminative network for realizing a semantic measure for reconstruction quality while using a generative network as a function approximator to model the inverse of hologram formation. |
Xiwen Chen; Hao Wang; Abofazl Razi; Michael Kozicki; Christopher Mann; | arxiv-cs.IR | 2022-05-25 |
| 91 | Misleading Deep-Fake Detection with GAN Fingerprints Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: These attacks, however, still require certain conditions to hold, such as interacting with the detection method or adjusting the GAN directly. In this paper, we introduce a novel class of simple counterattacks that overcomes these limitations. |
Vera Wesselkamp; Konrad Rieck; Daniel Arp; Erwin Quiring; | arxiv-cs.CV | 2022-05-25 |
| 92 | Learning Distributions By Generative Adversarial Networks: Approximation and Generalization Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study how well generative adversarial networks (GAN) learn probability distributions from finite samples by analyzing the convergence rates of these models. |
Yunfei Yang; | arxiv-cs.LG | 2022-05-25 |
| 93 | Highly Accurate FMRI ADHD Classification Using Time Distributed Multi Modal 3D CNNs Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work proposes an algorithm for fMRI data analysis for the classification of ADHD disorders. |
Christopher Sims; | arxiv-cs.LG | 2022-05-24 |
| 94 | Diffuse Map Guiding Unsupervised Generative Adversarial Network for SVBRDF Estimation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we use the Cook-Torrance model to reconstruct the materials. |
Zhiyao Luo; Hongnan Chen; | arxiv-cs.CV | 2022-05-24 |
| 95 | Time-series Transformer Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Many downstream tasks learn to model conditional distributions of the time-series, hence, synthetic data drawn from a generative model must satisfy 1) in addition to performing 2). We present TsT-GAN, a framework that capitalises on the Transformer architecture to satisfy the desiderata and compare its performance against five state-of-the-art models on five datasets and show that TsT-GAN achieves higher predictive performance on all datasets. |
Padmanaba Srinivasan; William J. Knottenbelt; | arxiv-cs.LG | 2022-05-23 |
| 96 | RCC-GAN: Regularized Compound Conditional GAN for Large-Scale Tabular Data Synthesis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a novel generative adversarial network (GAN) for synthesizing large-scale tabular databases which contain various features such as continuous, discrete, and binary. |
MOHAMMAD ESMAEILPOUR et. al. | arxiv-cs.LG | 2022-05-23 |
| 97 | Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, our focus is in falsifying systems with multiple requirements. |
Jarkko Peltomäki; Ivan Porres; | arxiv-cs.LG | 2022-05-23 |
| 98 | Wasserstein Generative Adversarial Networks for Online Test Generation for Cyber Physical Systems Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel online test generation algorithm WOGAN based on Wasserstein Generative Adversarial Networks. |
Jarkko Peltomäki; Frankie Spencer; Ivan Porres; | arxiv-cs.LG | 2022-05-23 |
| 99 | WOGAN at The SBST 2022 CPS Tool Competition Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: WOGAN is an online test generation algorithm based on Wasserstein generative adversarial networks. In this note, we present how WOGAN works and summarize its performance in the SBST 2022 CPS tool competition concerning the AI of a self-driving car. |
Jarkko Peltomäki; Frankie Spencer; Ivan Porres; | arxiv-cs.RO | 2022-05-23 |
| 100 | Collaborative Adversarial Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we highlight that some collaborative examples, nearly perceptually indistinguishable from both adversarial and benign examples yet show extremely lower prediction loss, can be utilized to enhance adversarial training. |
Qizhang Li; Yiwen Guo; Wangmeng Zuo; Hao Chen; | arxiv-cs.LG | 2022-05-23 |
| 101 | Cycle-GAN for Eye-tracking Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This manuscript presents a not typical implementation of the cycle generative adversarial networks (Cycle-GAN) method for eye-tracking tasks. |
Ildar Rakhmatulin; | arxiv-cs.CV | 2022-05-21 |
| 102 | Exploring The Trade-off Between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations Using Multi-objective Optimization Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we argue that automated counterfactual generation should regard several aspects of the produced adversarial instances, not only their adversarial capability. To this end, we present a novel framework for the generation of counterfactual examples which formulates its goal as a multi-objective optimization problem balancing three different objectives: 1) plausibility, i.e., the likeliness of the counterfactual of being possible as per the distribution of the input data; 2) intensity of the changes to the original input; and 3) adversarial power, namely, the variability of the model’s output induced by the counterfactual. |
Javier Del Ser; Alejandro Barredo-Arrieta; Natalia Díaz-Rodríguez; Francisco Herrera; Andreas Holzinger; | arxiv-cs.LG | 2022-05-20 |
| 103 | On Trace of PGD-Like Adversarial Attacks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, we construct Adversarial Response Characteristics (ARC) features to reflect the model’s gradient consistency around the input to indicate the extent of linearity. |
Mo Zhou; Vishal M. Patel; | arxiv-cs.CV | 2022-05-19 |
| 104 | Defending Against Adversarial Attacks By Energy Storage Facility Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this research, we manifest that the adversarial algorithm attack induces a significant cost-increase risk which will be exacerbated by the growing penetration of intermittent renewable energy. |
Jiawei Li; Jianxiao Wang; Lin Chen; Yang Yu; | arxiv-cs.CR | 2022-05-19 |
| 105 | Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the generative adversarial physics-informed neural network (GA-PINN), which integrates the generative adversarial (GA) mechanism with the structure of PINNs, to improve the performance of PINNs by exploiting only a small size of exact solutions to the PDEs. |
Wensheng Li; Chao Zhang; Chuncheng Wang; Hanting Guan; Dacheng Tao; | arxiv-cs.LG | 2022-05-18 |
| 106 | LogiGAN: Learning Logical Reasoning Via Adversarial Pre-training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models. |
Xinyu Pi; Wanjun Zhong; Yan Gao; Nan Duan; Jian-Guang Lou; | arxiv-cs.CL | 2022-05-18 |
| 107 | Passive Defense Against 3D Adversarial Point Clouds Through The Lens of 3D Steganalysis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: These point manipulations would modify geometrical properties and local correlations of benign point clouds more or less. Motivated by this basic fact, we propose to defend such adversarial examples with the aid of 3D steganalysis techniques. |
Jiahao Zhu; | arxiv-cs.MM | 2022-05-18 |
| 108 | Semantically Accurate Super-Resolution Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel architecture and domain-specific feature loss, allowing super-resolution to operate as a pre-processing step to increase the performance of downstream computer vision tasks, specifically semantic segmentation. |
Tristan Frizza; Donald G. Dansereau; Nagita Mehr Seresht; Michael Bewley; | arxiv-cs.CV | 2022-05-17 |
| 109 | Diffusion Models for Adversarial Purification Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose DiffPure that uses diffusion models for adversarial purification: Given an adversarial example, we first diffuse it with a small amount of noise following a forward diffusion process, and then recover the clean image through a reverse generative process. |
WEILI NIE et. al. | arxiv-cs.LG | 2022-05-16 |
| 110 | Transferability of Adversarial Attacks on Synthetic Speech Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: 3) The effect of clipping or self-padding operation on the transferability of adversarial attacks. By performing these analyses, we summarise the weaknesses of synthetic speech detectors and the transferability behaviours of adversarial attacks, which provide insights for future research. |
Jiacheng Deng; Shunyi Chen; Li Dong; Diqun Yan; Rangding Wang; | arxiv-cs.SD | 2022-05-16 |
| 111 | Generation of Non-stationary Stochastic Fields Using Generative Adversarial Networks with Limited Training Data Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate the problem of training Generative Adversarial Networks (GANs) models against a dataset of geological channelized patterns that has a few non-stationary spatial modes and examine the training and self-conditioning settings that improve the generalization capability at new spatial modes that were never seen in the given training set. |
Alhasan Abdellatif; Ahmed H. Elsheikh; Daniel Busby; Philippe Berthet; | arxiv-cs.LG | 2022-05-11 |
| 112 | Alternative Data Augmentation for Industrial Monitoring Using Adversarial Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this article, a novel strategy is proposed to augment small image datasets. |
Silvan Mertes; Andreas Margraf; Steffen Geinitz; Elisabeth André; | arxiv-cs.CV | 2022-05-09 |
| 113 | Data-Free Adversarial Knowledge Distillation for Graph Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, most of the existing KD methods require a large volume of real data, which are not readily available in practice, and may preclude their applicability in scenarios where the teacher model is trained on rare or hard to acquire datasets. To address this problem, we propose the first end-to-end framework for data-free adversarial knowledge distillation on graph structured data (DFAD-GNN). |
Yuanxin Zhuang; Lingjuan Lyu; Chuan Shi; Carl Yang; Lichao Sun; | arxiv-cs.LG | 2022-05-08 |
| 114 | Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To better exploit the powerful generative capability of GAN for real-world face SR, in this paper, we establish two independent degradation branches in the forward and backward cycle-consistent reconstruction processes, respectively, while the two processes share the same restoration branch. |
HAO HOU et. al. | arxiv-cs.CV | 2022-05-08 |
| 115 | End-to-End Rubbing Restoration Using Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the RubbingGAN model for restoring incomplete rubbing characters. |
Gongbo Sun; Zijie Zheng; Ming Zhang; | arxiv-cs.CV | 2022-05-07 |
| 116 | Generative Adversarial Neural Operators Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we instantiate GANO using the Wasserstein criterion and show how the Wasserstein loss can be computed in infinite-dimensional spaces. |
Md Ashiqur Rahman; Manuel A. Florez; Anima Anandkumar; Zachary E. Ross; Kamyar Azizzadenesheli; | arxiv-cs.LG | 2022-05-06 |
| 117 | LatentKeypointGAN: Controlling Images Via Latent Keypoints — Extended Abstract Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce LatentKeypointGAN, a two-stage GAN internally conditioned on a set of keypoints and associated appearance embeddings providing control of the position and style of the generated objects and their respective parts. |
Xingzhe He; Bastian Wandt; Helge Rhodin; | arxiv-cs.CV | 2022-05-06 |
| 118 | Text to Artistic Image Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Painting is one of the ways for people to express their ideas, but what if people with disabilities in hands want to paint? To tackle this challenge, we create an end-to-end solution that can generate artistic images from text descriptions. |
Qinghe Tian; Jean-Claude Franchitti; | arxiv-cs.CV | 2022-05-05 |
| 119 | Subverting Fair Image Search with Generative Adversarial Perturbations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we explore the intersection fairness and robustness in the context of ranking: when a ranking model has been calibrated to achieve some definition of fairness, is it possible for an external adversary to make the ranking model behave unfairly without having access to the model or training data? |
Avijit Ghosh; Matthew Jagielski; Christo Wilson; | arxiv-cs.LG | 2022-05-04 |
| 120 | Wasserstein Adversarial Learning Based Temporal Knowledge Graph Embedding Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new temporal knowledge graph embedding framework by introducing adversarial learning to further refine the performance of traditional TKGE models. |
Yuanfei Dai; Wenzhong Guo; Carsten Eickhoff; | arxiv-cs.IR | 2022-05-03 |
| 121 | ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a practical unsupervised anomaly-based deep learning detection system called ARCADE (Adversarially Regularized Convolutional Autoencoder for unsupervised network anomaly DEtection). |
Willian T. Lunardi; Martin Andreoni Lopez; Jean-Pierre Giacalone; | arxiv-cs.LG | 2022-05-03 |
| 122 | Splicing Detection and Localization In Satellite Imagery Using Conditional GANs IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Satellite images specifically can be modified in a number of ways, including the insertion of objects to hide existing scenes and structures. In this paper, we describe the use of a Conditional Generative Adversarial Network (cGAN) to identify the presence of such spliced forgeries within satellite images. |
EMILY R. BARTUSIAK et. al. | arxiv-cs.CV | 2022-05-03 |
| 123 | Enhancing Adversarial Training with Feature Separability Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we observe two major shortcomings of the features learned by existing adversarial training methods:(1) low intra-class feature similarity; and (2) conservative inter-classes feature variance. To overcome these shortcomings, we introduce a new concept of adversarial training graph (ATG) with which the proposed adversarial training with feature separability (ATFS) enables to coherently boost the intra-class feature similarity and increase inter-class feature variance. |
Yaxin Li; Xiaorui Liu; Han Xu; Wentao Wang; Jiliang Tang; | arxiv-cs.CV | 2022-05-02 |
| 124 | Logically Consistent Adversarial Attacks for Soft Theorem Provers Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Recent efforts within the AI community have yielded impressive results towards soft theorem proving over natural language sentences using language models. We propose a novel, generative adversarial framework for probing and improving these models’ reasoning capabilities. |
Alexander Gaskell; Yishu Miao; Lucia Specia; Francesca Toni; | arxiv-cs.LG | 2022-04-29 |
| 125 | Curvature Graph Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed a novel Curvature Graph Generative Adversarial Networks method, named CurvGAN, which is the first GAN-based graph representation method in the Riemannian geometric manifold. |
JIANXIN LI et. al. | www | 2022-04-29 |
| 126 | Generative Session-based Recommendation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Previous models mostly focus on designing different models to fit the observed data, which can be quite sparse in real-world scenarios. To alleviate this problem, in this paper, we propose a novel generative session-based recommendation framework. |
ZHIDAN WANG et. al. | www | 2022-04-29 |
| 127 | Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We observe that: (i) rain is a mixture of rain streaks and rainy haze; (ii) the scene depth determines the intensity of rain streaks and the transformation into the rainy haze; (iii) most existing deraining methods are only trained on synthetic rainy images, and hence generalize poorly to the real-world scenes. Motivated by these observations, we propose a new SEMI-supervised Mixture Of rain REmoval Generative Adversarial Network (Semi-MoreGAN), which consists of four key modules: (I) a novel attentional depth prediction network to provide precise depth estimation; (ii) a context feature prediction network composed of several well-designed detailed residual blocks to produce detailed image context features; (iii) a pyramid depth-guided non-local network to effectively integrate the image context with the depth information, and produce the final rain-free images; and (iv) a comprehensive semi-supervised loss function to make the model not limited to synthetic datasets but generalize smoothly to real-world heavy rainy scenes. |
YIYANG SHEN et. al. | arxiv-cs.CV | 2022-04-28 |
| 128 | Defending Against Person Hiding Adversarial Patch Attack with A Universal White Frame Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To satisfy the aforementioned properties, we propose a novel pattern optimization algorithm that can defend against the adversarial patch. |
Youngjoon Yu; Hong Joo Lee; Hakmin Lee; Yong Man Ro; | arxiv-cs.CV | 2022-04-27 |
| 129 | Restricted Black-box Adversarial Attack Against DeepFake Face Swapping Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specially, we propose the Transferable Cycle Adversary Generative Adversarial Network (TCA-GAN) to construct the adversarial perturbation for disrupting unknown DeepFake systems. |
Junhao Dong; Yuan Wang; Jianhuang Lai; Xiaohua Xie; | arxiv-cs.CV | 2022-04-26 |
| 130 | Intercategorical Label Interpolation for Emotional Face Generation with Conditional Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This greatly hinders the learning and modeling of smooth transitions between displayed affective states. To overcome this challenge, we explore the potential of label interpolation to enhance networks trained on categorical datasets with the ability to generate images conditioned on continuous features. |
SILVAN MERTES et. al. | arxiv-cs.CV | 2022-04-26 |
| 131 | Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, the existing adversarial example does not have recoverability for serving as an effective protection mechanism. To address this issue, we propose a recoverable generative adversarial network to generate self-recoverable adversarial examples. |
Jiawei Zhang; Jinwei Wang; Hao Wang; Xiangyang Luo; | arxiv-cs.CV | 2022-04-25 |
| 132 | Evolutionary Latent Space Search for Driving Human Portrait Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This article presents an evolutionary approach for synthetic human portraits generation based on the latent space exploration of a generative adversarial network. |
Benjamín Machín; Sergio Nesmachnow; Jamal Toutouh; | arxiv-cs.CV | 2022-04-25 |
| 133 | When Adversarial Examples Are Excusable Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we analyze both test errors and adversarial errors on a well controlled but highly non-linear visual classification problem. |
Pieter-Jan Kindermans; Charles Staats; | arxiv-cs.LG | 2022-04-25 |
| 134 | A Note on The Regularity of Images Generated By Convolutional Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: The regularity of images generated by convolutional neural networks, such as the U-net, generative adversarial networks, or the deep image prior, is analyzed. In a … |
Andreas Habring; Martin Holler; | arxiv-cs.CV | 2022-04-22 |
| 135 | 6GAN: IPv6 Multi-Pattern Target Generation Via Generative Adversarial Nets with Reinforcement Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce 6GAN, a novel architecture built with Generative Adversarial Net (GAN) and reinforcement learning for multi-pattern target generation. |
TIANYU CUI et. al. | arxiv-cs.NI | 2022-04-20 |
| 136 | DAM-GAN : Image Inpainting Using Dynamic Attention Map Based on Fake Texture Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To reduce pixel inconsistency disorder resulted from fake textures, we introduce a GAN-based model using dynamic attention map (DAM-GAN). |
Dongmin Cha; Daijin Kim; | arxiv-cs.CV | 2022-04-20 |
| 137 | Imbalanced Classification Via A Tabular Translation GAN Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a model based on Generative Adversarial Networks which uses additional regularization losses to map majority samples to corresponding synthetic minority samples. |
Jonathan Gradstein; Moshe Salhov; Yoav Tulpan; Ofir Lindenbaum; Amir Averbuch; | arxiv-cs.LG | 2022-04-19 |
| 138 | CorrGAN: Input Transformation Technique Against Natural Corruptions Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose CorrGAN approach, which can generate benign input when a corrupted input is provided. |
Mirazul Haque; Christof J. Budnik; Wei Yang; | arxiv-cs.LG | 2022-04-18 |
| 139 | DR-GAN: Distribution Regularization for Text-to-Image Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a new Text-to-Image generation model, named Distribution Regularization Generative Adversarial Network (DR-GAN), to generate images from text descriptions from improved distribution learning. |
Hongchen Tan; Xiuping Liu; Baocai Yin; Xin Li; | arxiv-cs.CV | 2022-04-17 |
| 140 | SETTI: A Self-supervised Adversarial Malware Detection Architecture in An IoT Environment Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by this, in this paper we propose an adversarial self-supervised architecture for detecting malware in IoT networks, SETTI, considering samples of IoT network traffic that may not be labeled. |
Marjan Golmaryami; Rahim Taheri; Zahra Pooranian; Mohammad Shojafar; Pei Xiao; | arxiv-cs.CR | 2022-04-16 |
| 141 | Synthesizing Informative Training Samples with GAN Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel method to synthesize Informative Training samples with GAN (IT-GAN). |
Bo Zhao; Hakan Bilen; | arxiv-cs.LG | 2022-04-15 |
| 142 | PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents PLGAN, a simple yet effective method based on generative adversarial networks, to segment power lines from aerial images with different backgrounds. |
Rabab Abdelfattah; Xiaofeng Wang; Song Wang; | arxiv-cs.CV | 2022-04-14 |
| 143 | Robotic and Generative Adversarial Attacks in Offline Writer-independent Signature Verification Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study explores how robots and generative approaches can be used to mount successful false-acceptance adversarial attacks on signature verification systems. |
Jordan J. Bird; | arxiv-cs.RO | 2022-04-14 |
| 144 | Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a learning-based sampling strategy for constrained motion planning problems. |
Cihan Acar; Keng Peng Tee; | arxiv-cs.RO | 2022-04-14 |
| 145 | Q-TART: Quickly Training for Adversarial Robustness and In-Transferability Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to simultaneously tackle Performance, Efficiency, and Robustness, using our proposed algorithm Q-TART, Quickly Train for Adversarial Robustness and in-Transferability. |
Madan Ravi Ganesh; Salimeh Yasaei Sekeh; Jason J. Corso; | arxiv-cs.CV | 2022-04-14 |
| 146 | Liuer Mihou: A Practical Framework for Generating and Evaluating Grey-box Adversarial Attacks Against NIDS Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents the Liuer Mihou attack that generates practical and replayable adversarial network packets that can bypass anomaly-based NIDS deployed in the Internet of Things (IoT) networks. |
KE HE et. al. | arxiv-cs.CR | 2022-04-12 |
| 147 | Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents an overview of the evolution of GAN architectures followed by a systematic review of their application to agriculture (https://github.com/Derekabc/GANs-Agriculture), involving various vision tasks for plant health, weeds, fruits, aquaculture, animal farming, plant phenotyping as well as postharvest detection of fruit defects. |
Ebenezer Olaniyi; Dong Chen; Yuzhen Lu; Yanbo Huang; | arxiv-cs.CV | 2022-04-10 |
| 148 | SnapMode: An Intelligent and Distributed Large-Scale Fashion Image Retrieval Platform Based On Big Data and Deep Generative Adversarial Network Technologies Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The role of the proposed platform is then described in developing a disentangled feature extraction method by employing deep convolutional generative adversarial networks (DCGANs) for content-based image indexing and retrieval. |
Narges Norouzi; Reza Azmi; Sara Saberi Tehrani Moghadam; Maral Zarvani; | arxiv-cs.IR | 2022-04-08 |
| 149 | Instance Segmentation of Unlabeled Modalities Via Cyclic Segmentation GAN Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel Cyclic Segmentation Generative Adversarial Network (CySGAN) that conducts image translation and instance segmentation jointly using a unified framework. |
LEANDER LAUENBURG et. al. | arxiv-cs.CV | 2022-04-06 |
| 150 | Distributed Statistical Min-Max Learning in The Presence of Byzantine Agents Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our main contribution is to provide a crisp analysis of the proposed robust extra-gradient algorithm for smooth convex-concave and smooth strongly convex-strongly concave functions. |
Arman Adibi; Aritra Mitra; George J. Pappas; Hamed Hassani; | arxiv-cs.LG | 2022-04-06 |
| 151 | Learning to Generate Realistic Noisy Images Via Pixel-level Noise-aware Adversarial Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Subsequently, we propose a novel framework, namely Pixel-level Noise-aware Generative Adversarial Network (PNGAN). |
YUANHAO CAI et. al. | arxiv-cs.CV | 2022-04-06 |
| 152 | Optimization Models and Interpretations for Three Types of Adversarial Perturbations Against Support Vector Machines Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the optimization models and the interpretations for three types of adversarial perturbations against support vector machines, including sample-adversarial perturbations (sAP), class-universal adversarial perturbations (cuAP) as well as universal adversarial perturbations (uAP). |
Wen Su; Qingna Li; Chunfeng Cui; | arxiv-cs.LG | 2022-04-06 |
| 153 | GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Addressing the challenges, for the first time, we introduce expert knowledge to guide the agent to make better decisions in RL-based PT and propose a Generative Adversarial Imitation Learning-based generic intelligent Penetration testing framework, denoted as GAIL-PT, to solve the problems of higher labor costs due to the involvement of security experts and high-dimensional discrete action space. |
Jinyin Chen; Shulong Hu; Haibin Zheng; Changyou Xing; Guomin Zhang; | arxiv-cs.CR | 2022-04-05 |
| 154 | SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, to scale to complex settings, many autonomous driving systems combine fixed, safe, optimization-based low-level controllers with high-level decision-making logic that selects the appropriate task and associated controller. In this paper, we attempt to bridge this gap in complexity by employing Safety-Aware Hierarchical Adversarial Imitation Learning (SHAIL), a method for learning a high-level policy that selects from a set of low-level controller instances in a way that imitates low-level driving data on-policy. |
Arec Jamgochian; Etienne Buehrle; Johannes Fischer; Mykel J. Kochenderfer; | arxiv-cs.RO | 2022-04-04 |
| 155 | DAD: Data-free Adversarial Defense at Test Time Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Also, data curation is expensive and companies may have proprietary rights over it. To handle such situations, we propose a completely novel problem of ‘test-time adversarial defense in absence of training data and even their statistics’. |
Gaurav Kumar Nayak; Ruchit Rawal; Anirban Chakraborty; | arxiv-cs.LG | 2022-04-04 |
| 156 | Detecting In-vehicle Intrusion Via Semi-supervised Learning-based Convolutional Adversarial Autoencoders Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Although machine learning methods have many advantages for IDS, previous models usually require a large amount of labeled data, which results in high time and labor costs. To handle this problem, we propose a novel semi-supervised learning-based convolutional adversarial autoencoder model in this paper. |
Thien-Nu Hoang; Daehee Kim; | arxiv-cs.CR | 2022-04-03 |
| 157 | StyleWaveGAN: Style-based Synthesis of Drum Sounds with Extensive Controls Using Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we introduce StyleWaveGAN, a style-based drum sound generator that is a variation of StyleGAN, a state-of-the-art image generator. |
Antoine Lavault; Axel Roebel; Matthieu Voiry; | arxiv-cs.SD | 2022-04-02 |
| 158 | Adversarial Neon Beam: Robust Physical-World Adversarial Attack to DNNs Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an attack method called adversarial neon beam (AdvNB), which can execute the physical attack by obtaining the physical parameters of adversarial neon beams with very few queries. |
Chengyin Hu; Kalibinuer Tiliwalidi; | arxiv-cs.CV | 2022-04-02 |
| 159 | DAG-WGAN: Causal Structure Learning With Wasserstein Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a new model named DAG-WGAN, which combines the Wasserstein-based adversarial loss, an auto-encoder architecture together with an acyclicity constraint. |
Hristo Petkov; Colin Hanley; Feng Dong; | arxiv-cs.LG | 2022-04-01 |
| 160 | Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in The Latent Space Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel approach for guiding a Generative Adversarial Network trained on the FashionGen dataset to generate designs corresponding to target fashion styles. |
Imke Grabe; Jichen Zhu; Manex Agirrezabal; | arxiv-cs.NE | 2022-04-01 |
| 161 | Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a generative adversarial network to simulate noisy spectrum from the clean spectrum (Simu-GAN), where only 10 minutes of unparalleled in-domain noisy speech data is required as labels. |
Chen Chen; Nana Hou; Yuchen Hu; Shashank Shirol; Eng Siong Chng; | arxiv-cs.SD | 2022-03-29 |
| 162 | TransductGAN: A Transductive Adversarial Model for Novelty Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we propose TransductGAN, a transductive generative adversarial network that attempts to learn how to generate image examples from both the novel and negative classes by using a mixture of two Gaussians in the latent space. |
Najiba Toron; Janaina Mourao-Miranda; John Shawe-Taylor; | arxiv-cs.LG | 2022-03-29 |
| 163 | Infrared and Visible Image Fusion Via Interactive Compensatory Attention Adversarial Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Toward this end, we propose a novel end-to-end mode based on generative adversarial training to achieve better fusion balance, termed as \textit{interactive compensatory attention fusion network} (ICAFusion). |
Zhishe Wang; Wenyu Shao; Yanlin Chen; Jiawei Xu; Xiaoqin Zhang; | arxiv-cs.CV | 2022-03-29 |
| 164 | Synthesis and Execution of Communicative Robotic Movements with Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects, aiming to endow robots with the capability to show carefulness in their movements. |
LUCA GARELLO et. al. | arxiv-cs.RO | 2022-03-29 |
| 165 | Photographic Visualization of Weather Forecasts with Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We therefore introduce a novel method that uses photographic images to also visualize future weather conditions. |
Christian Sigg; Flavia Cavallaro; Tobias Günther; Martin R. Oswald; | arxiv-cs.CV | 2022-03-29 |
| 166 | Treatment Learning Transformer for Noisy Image Classification Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we incorporate this binary information of existence of noise as treatment into image classification tasks to improve prediction accuracy by jointly estimating their treatment effects. |
Chao-Han Huck Yang; I-Te Danny Hung; Yi-Chieh Liu; Pin-Yu Chen; | arxiv-cs.CV | 2022-03-29 |
| 167 | Conjugate Gradient Method for Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Since this optimization is more difficult than minimization of a single objective function, we propose to apply the conjugate gradient method to solve the local Nash equilibrium problem in GANs. |
Hiroki Naganuma; Hideaki Iiduka; | arxiv-cs.LG | 2022-03-28 |
| 168 | A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a comprehensive survey trying to offer a systematic and structured investigation on robust adversarial training in pattern recognition. |
Zhuang Qian; Kaizhu Huang; Qiu-Feng Wang; Xu-Yao Zhang; | arxiv-cs.CV | 2022-03-26 |
| 169 | Reverse Engineering of Imperceptible Adversarial Image Perturbations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: By integrating these RED principles with image denoising, we propose a new Class-Discriminative Denoising based RED framework, termed CDD-RED. |
YIFAN GONG et. al. | arxiv-cs.CV | 2022-03-26 |
| 170 | From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the exponential information metric into the GAN, referred to as MIM-based GAN, whose superior characteristics on data generation are discussed in theory. |
Rui She; Pingyi Fan; | arxiv-cs.LG | 2022-03-25 |
| 171 | Fast and Computationally Efficient Generative Adversarial Network Algorithm for Unmanned Aerial Vehicle-based Network Coverage Optimization Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Considering the tremendous potential of unmanned aerial vehicles in the future, we propose a new heuristic algorithm for coverage optimization. |
MAREK RUŽIČKA et. al. | arxiv-cs.LG | 2022-03-25 |
| 172 | A Unified Contrastive Energy-based Model for Understanding The Generative Ability of Adversarial Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we demystify this phenomenon by developing a unified probabilistic framework, called Contrastive Energy-based Models (CEM). |
Yifei Wang; Yisen Wang; Jiansheng Yang; Zhouchen Lin; | arxiv-cs.LG | 2022-03-25 |
| 173 | Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel feature-level adversarial training method named FLAT. |
Hanjie Chen; Yangfeng Ji; | arxiv-cs.CL | 2022-03-23 |
| 174 | Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Method: We introduce an approach called Dazzle which is an optimized version of conditional Wasserstein Generative Adversarial Networks with gradient penalty (cWGAN-GP). |
Rui Shu; Tianpei Xia; Laurie Williams; Tim Menzies; | arxiv-cs.CR | 2022-03-21 |
| 175 | Generative Adversarial Network for Future Hand Segmentation from Egocentric Video Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the novel problem of anticipating a time series of future hand masks from egocentric video. |
Wenqi Jia; Miao Liu; James M. Rehg; | arxiv-cs.CV | 2022-03-21 |
| 176 | Review of Disentanglement Approaches for Medical Applications — Towards Solving The Gordian Knot of Generative Models in Healthcare Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we give a comprehensive overview of popular generative models, like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Flow-based Models. |
Jana Fragemann; Lynton Ardizzone; Jan Egger; Jens Kleesiek; | arxiv-cs.LG | 2022-03-21 |
| 177 | FGAN: Federated Generative Adversarial Networks for Anomaly Detection in Network Traffic Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The solution proposed in this work aims at tackling the above two issues by using GANs in a federated architecture in networks of such scale and capacity. |
Sankha Das; | arxiv-cs.CR | 2022-03-21 |
| 178 | NeuralReshaper: Single-image Human-body Retouching with Deep Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present NeuralReshaper, a novel method for semantic reshaping of human bodies in single images using deep generative networks. |
Beijia Chen; Hongbo Fu; Xiang Chen; Kun Zhou; Youyi Zheng; | arxiv-cs.CV | 2022-03-20 |
| 179 | Font Generation with Missing Impression Labels Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a font generation model that is robust against missing impression labels. |
Seiya Matsuda; Akisato Kimura; Seiichi Uchida; | arxiv-cs.CV | 2022-03-19 |
| 180 | Modelling Nonlinear Dependencies in The Latent Space of Inverse Scattering Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This motivates using models for this distribution that allow for non-linear dependencies between variables. |
Juliusz Ziomek; Katayoun Farrahi; | arxiv-cs.CV | 2022-03-19 |
| 181 | AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: From the perspective of neural network pruning, we propose a novel end-to-end sparse adversarial attack method, namely AutoAdversary, which can find the most important pixels automatically by integrating the pixel selection into the adversarial attack. |
Jinqiao Li; Xiaotao Liu; Jian Zhao; Furao Shen; | arxiv-cs.CV | 2022-03-18 |
| 182 | AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models Against Adversarial Volumetric Attacks on IoT Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present AdIoTack, a system that highlights vulnerabilities of decision trees against adversarial attacks, helping cybersecurity teams quantify and refine the resilience of their trained models for monitoring IoT networks. |
Arman Pashamokhtari; Gustavo Batista; Hassan Habibi Gharakheili; | arxiv-cs.LG | 2022-03-18 |
| 183 | Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to generate adversarial samples by modifying activations of upper layers encoding semantically meaningful concepts. |
Johannes Schneider; Giovanni Apruzzese; | arxiv-cs.LG | 2022-03-18 |
| 184 | Defending Variational Autoencoders from Adversarial Attacks with MCMC Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we examine several objective functions for adversarial attacks construction, suggest metrics assess the model robustness, and propose a solution to alleviate the effect of an attack. |
Anna Kuzina; Max Welling; Jakub M. Tomczak; | arxiv-cs.LG | 2022-03-18 |
| 185 | Fine Detailed Texture Learning for 3D Meshes with Generative Models Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a method to reconstruct high-quality textured 3D models from both multi-view and single-view images. |
Aysegul Dundar; Jun Gao; Andrew Tao; Bryan Catanzaro; | arxiv-cs.CV | 2022-03-17 |
| 186 | Image Super-Resolution With Deep Variational Autoencoders Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce VDVAE-SR, a new model that aims to exploit the most recent deep VAE methodologies to improve upon image super-resolution using transfer learning on pretrained VDVAEs. |
Darius Chira; Ilian Haralampiev; Ole Winther; Andrea Dittadi; Valentin Liévin; | arxiv-cs.CV | 2022-03-17 |
| 187 | Generating Unrepresented Proportions of Geological Facies Using Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate the capacity of Generative Adversarial Networks (GANs) in interpolating and extrapolating facies proportions in a geological dataset. |
Alhasan Abdellatif; Ahmed H. Elsheikh; Gavin Graham; Daniel Busby; Philippe Berthet; | arxiv-cs.LG | 2022-03-17 |
| 188 | On The Properties of Adversarially-Trained CNNs Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we describe surprising properties of adversarially-trained models, shedding light on mechanisms through which robustness against adversarial attacks is implemented. |
Mattia Carletti; Matteo Terzi; Gian Antonio Susto; | arxiv-cs.CV | 2022-03-17 |
| 189 | PPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a gradually shrinking GAN (PPCD-GAN) by introducing progressive pruning residual block (PP-Res) and class-aware distillation. |
Duc Minh Vo; Akihiro Sugimoto; Hideki Nakayama; | arxiv-cs.CV | 2022-03-16 |
| 190 | Robustness Through Cognitive Dissociation Mitigation in Contrastive Adversarial Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a novel neural network training framework that increases model’s adversarial robustness to adversarial attacks while maintaining high clean accuracy by combining contrastive learning (CL) with adversarial training (AT). |
Adir Rahamim; Itay Naeh; | arxiv-cs.LG | 2022-03-16 |
| 191 | Driving Anomaly Detection Using Conditional Generative Adversarial Network Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study proposes an unsupervised method to quantify driving anomalies using a conditional generative adversarial network (GAN). |
Yuning Qiu; Teruhisa Misu; Carlos Busso; | arxiv-cs.CV | 2022-03-15 |
| 192 | Neural Radiance Projection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The proposed method, Neural Radiance Projection (NeRP), addresses the three most fundamental shortages of training such a convolutional neural network on X-ray image segmentation: dealing with missing/limited human-annotated datasets; ambiguity on the per-pixel label; and the imbalance across positive- and negative- classes distribution. |
Pham Ngoc Huy; Tran Minh Quan; | arxiv-cs.CV | 2022-03-15 |
| 193 | Adversarial Counterfactual Augmentation: Application in Alzheimer’s Disease Classification Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Different from these approaches, in this work, we propose a novel adversarial counterfactual augmentation scheme that aims to find the most \textit{effective} counterfactuals to improve downstream tasks with a pre-trained generative model. |
Tian Xia; Pedro Sanchez; Chen Qin; Sotirios A. Tsaftaris; | arxiv-cs.CV | 2022-03-15 |
| 194 | Generating Privacy-Preserving Process Data with Deep Generative Models Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduced an adversarial generative network for process data generation (ProcessGAN) with two Transformer networks for the generator and the discriminator. |
Keyi Li; Sen Yang; Travis M. Sullivan; Randall S. Burd; Ivan Marsic; | arxiv-cs.LG | 2022-03-15 |
| 195 | A Review of Generative Adversarial Networks for Electronic Health Records: Applications, Evaluation Measures and Data Sources Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work aims to review the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. |
Ghadeer Ghosheh; Jin Li; Tingting Zhu; | arxiv-cs.LG | 2022-03-14 |
| 196 | On The Benefits of Knowledge Distillation for Adversarial Robustness Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To that end, we present Adversarial Knowledge Distillation (AKD), a new framework to improve a model’s robust performance, consisting on adversarially training a student on a mixture of the original labels and the teacher outputs. |
Javier Maroto; Guillermo Ortiz-Jiménez; Pascal Frossard; | arxiv-cs.LG | 2022-03-14 |
| 197 | Defending From Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents Z-Mask, a robust and effective strategy to improve the adversarial robustness of convolutional networks against physically-realizable adversarial attacks. |
Giulio Rossolini; Federico Nesti; Fabio Brau; Alessandro Biondi; Giorgio Buttazzo; | arxiv-cs.CV | 2022-03-14 |
| 198 | Generating Practical Adversarial Network Traffic Flows Using NIDSGAN Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we ask the practical question of whether real-world ML-based NIDS can be circumvented by crafted adversarial flows, and if so, how can they be created. |
BOLOR-ERDENE ZOLBAYAR et. al. | arxiv-cs.CR | 2022-03-13 |
| 199 | TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulence Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a self-supervised and self-calibrating multi-shot approach to imaging through atmospheric turbulence, called TurbuGAN. |
Brandon Y. Feng; Mingyang Xie; Christopher A. Metzler; | arxiv-cs.CV | 2022-03-13 |
| 200 | A Survey in Adversarial Defences and Robustness in NLP Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The proposed survey is an attempt to review different methods proposed for adversarial defenses in NLP in the recent past by proposing a novel taxonomy. |
Shreya Goyal; Sumanth Doddapaneni; Mitesh M. Khapra; Balaraman Ravindran; | arxiv-cs.CL | 2022-03-12 |
| 201 | Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a gradient-based adversarial attack against transformer-based text classifiers. |
Sahar Sadrizadeh; Ljiljana Dolamic; Pascal Frossard; | arxiv-cs.CL | 2022-03-11 |
| 202 | A High-precision Self-supervised Monocular Visual Odometry in Foggy Weather Based on Robust Cycled Generative Adversarial Networks and Multi-task Learning Aided Depth Estimation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a high-precision self-supervised monocular VO, which is specifically designed for navigation in foggy weather. |
Xiuyuan Li; Jiangang Yu; Fengchao Li; Guowen An; | arxiv-cs.CV | 2022-03-09 |
| 203 | FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Prediction Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: By leveraging the structure of response patterns, we propose a unified and flexible framework based on Generative Adversarial Nets (GAN) to deal with fragmentary data imputation and label prediction at the same time. |
Fang Fang; Shenliao Bao; | arxiv-cs.LG | 2022-03-09 |
| 204 | Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel federated adversarial learning framework for protecting both data and deep neural networks. |
YI CHANG et. al. | arxiv-cs.SD | 2022-03-09 |
| 205 | On Generative Models As The Basis for Digital Twins Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: A framework is proposed for generative models as a basis for digital twins or mirrors of structures. |
G. Tsialiamanis; D. J. Wagg; N. Dervilis; K. Worden; | arxiv-cs.LG | 2022-03-08 |
| 206 | Regularized Training of Intermediate Layers for Generative Models for Inverse Problems Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: For both of these inversion algorithms, we introduce a new regularized GAN training algorithm and demonstrate that the learned generative model results in lower reconstruction errors across a wide range of under sampling ratios when solving compressed sensing, inpainting, and super-resolution problems. |
Sean Gunn; Jorio Cocola; Paul Hand; | arxiv-cs.LG | 2022-03-08 |
| 207 | Machine Learning in NextG Networks Via Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate their use in next-generation (NextG) communications within the context of cognitive networks to address i) spectrum sharing, ii) detecting anomalies, and iii) mitigating security attacks. |
Ender Ayanoglu; Kemal Davaslioglu; Yalin E. Sagduyu; | arxiv-cs.LG | 2022-03-08 |
| 208 | Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work establishes the fundamental constraint levels required to achieve realism and introduces the Adaptative Perturbation Pattern Method (A2PM) to fulfill these constraints in a gray-box setting. |
João Vitorino; Nuno Oliveira; Isabel Praça; | arxiv-cs.CR | 2022-03-08 |
| 209 | DATGAN: Integrating Expert Knowledge Into Deep Learning for Synthetic Tabular Data Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This article presents the Directed Acyclic Tabular GAN (DATGAN) to address these limitations by integrating expert knowledge in deep learning models for synthetic tabular data generation. |
Gael Lederrey; Tim Hillel; Michel Bierlaire; | arxiv-cs.LG | 2022-03-07 |
| 210 | GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose the GANSpiration approach that suggests design examples for both targeted and serendipitous inspiration, leveraging a style-based Generative Adversarial Network. |
Mohammad Amin Mozaffari; Xinyuan Zhang; Jinghui Cheng; Jin L. C. Guo; | arxiv-cs.HC | 2022-03-07 |
| 211 | Adversarial Texture for Fooling Person Detectors in The Physical World Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a generative method, named Toroidal-Cropping-based Expandable Generative Attack (TC-EGA), to craft AdvTexture with repetitive structures. |
ZHANHAO HU et. al. | arxiv-cs.CV | 2022-03-07 |
| 212 | Semantic-Aware Latent Space Exploration for Face Image Restoration Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a semantic-aware latent space exploration method for image restoration (SAIR). |
Yanhui Guo; Fangzhou Luo; Xiaolin Wu; | arxiv-cs.CV | 2022-03-06 |
| 213 | Searching for Robust Neural Architectures Via Comprehensive and Reliable Evaluation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate the above problems, we propose a novel framework, called Auto Adversarial Attack and Defense (AAAD), where we employ neural architecture search methods, and four types of robustness evaluations are considered, including adversarial noise, natural noise, system noise and quantified metrics, thereby assisting in finding more robust architectures. |
JIALIANG SUN et. al. | arxiv-cs.LG | 2022-03-06 |
| 214 | Hybrid Deep Learning Model Using SPCAGAN Augmentation for Insider Threat Analysis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a linear manifold learning-based generative adversarial network, SPCAGAN, that takes input from heterogeneous data sources and adds a novel loss function to train the generator to produce high-quality data that closely resembles the original data distribution. |
R G Gayathri; Atul Sajjanhar; Yong Xiang; | arxiv-cs.CR | 2022-03-05 |
| 215 | Detecting High-Quality GAN-Generated Face Images Using Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this chapter, we propose a new strategy to differentiate GAN-generated images from authentic images by leveraging spectral band discrepancies, focusing on artificial face image synthesis. |
Ehsan Nowroozi; Mauro Conti; Yassine Mekdad; | arxiv-cs.CR | 2022-03-03 |
| 216 | Fail-Safe Generative Adversarial Imitation Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: For flexible yet safe imitation learning (IL), we propose a modular approach that uses a generative imitator policy with a safety layer, has an overall explicit density/gradient, can therefore be end-to-end trained using generative adversarial IL (GAIL), and comes with theoretical worst-case safety/robustness guarantees. |
Philipp Geiger; Christoph-Nikolas Straehle; | arxiv-cs.LG | 2022-03-03 |
| 217 | On Generating Parametrised Structural Data Using Conditional Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In the current work, with a view to confronting such issues, the generation of artificial data using a variation of the generative adversarial network (GAN) algorithm, is used. |
G. Tsialiamanis; D. J. Wagg; N. Dervilis; K. Worden; | arxiv-cs.LG | 2022-03-03 |
| 218 | Learning Category-Level Generalizable Object Manipulation Policy Via Generative Adversarial Self-Imitation Learning from Demonstrations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we tackle this category-level object manipulation policy learning problem via imitation learning in a task-agnostic manner, where we assume no handcrafted dense rewards but only a terminal reward. |
Hao Shen; Weikang Wan; He Wang; | arxiv-cs.RO | 2022-03-03 |
| 219 | Discriminating Against Unrealistic Interpolations in Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we establish that the discriminator can be used effectively to avoid regions of low sample quality along shortest paths. |
Henning Petzka; Ted Kronvall; Cristian Sminchisescu; | arxiv-cs.LG | 2022-03-02 |
| 220 | On The Application of Generative Adversarial Networks for Nonlinear Modal Analysis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In the current work, a machine learning scheme is proposed with a view to performing nonlinear modal analysis. |
G. Tsialiamanis; M. D. Champneys; N. Dervilis; D. J. Wagg; K. Worden; | arxiv-cs.LG | 2022-03-02 |
| 221 | PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a generative adversarial network for point cloud upsampling, which can not only make the upsampled points evenly distributed on the underlying surface but also efficiently generate clean high frequency regions. |
Hao Liu; Hui Yuan; Junhui Hou; Raouf Hamzaoui; Wei Gao; | arxiv-cs.CV | 2022-03-02 |
| 222 | Generative Adversarial Networks IF:2 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. |
Gilad Cohen; Raja Giryes; | arxiv-cs.CV | 2022-03-01 |
| 223 | Towards Creativity Characterization of Generative Models Via Group-based Subset Scanning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, incorporating research on human creativity into generative deep learning techniques presents an opportunity to make their outputs more compelling and human-like. |
CELIA CINTAS et. al. | arxiv-cs.CV | 2022-03-01 |
| 224 | MRI-GAN: A Generalized Approach to Detect DeepFakes Using Perceptual Image Assessment Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we describe our work to develop general, deep learning-based models to classify DeepFake content. |
Pratikkumar Prajapati; Chris Pollett; | arxiv-cs.CV | 2022-02-28 |
| 225 | OUR-GAN: One-shot Ultra-high-Resolution Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose OUR-GAN, the first one-shot ultra-high-resolution (UHR) image synthesis framework that generates non-repetitive images with 4K or higher resolution from a single training image. |
Donghwee Yoon; Junseok Oh; Hayeong Choi; Minjae Yi; Injung Kim; | arxiv-cs.CV | 2022-02-28 |
| 226 | Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel domain disentangled generative adversarial network (DD-GAN) for zero-shot sketch-based 3D retrieval, which can retrieve the unseen categories that are not accessed during training. |
Rui Xu; Zongyan Han; Le Hui; Jianjun Qian; Jin Xie; | arxiv-cs.CV | 2022-02-24 |
| 227 | Art Creation with Multi-Conditional StyleGANs Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a multi-conditional Generative Adversarial Network (GAN) approach trained on large amounts of human paintings to synthesize realistic-looking paintings that emulate human art. |
KONSTANTIN DOBLER et. al. | arxiv-cs.CV | 2022-02-23 |
| 228 | Generating Synthetic Mobility Networks with Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this article, we address mobility network generation, i.e., generating a city’s entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people’s movements between those locations, thus describing the entire mobility set flows within a city. |
Giovanni Mauro; Massimiliano Luca; Antonio Longa; Bruno Lepri; Luca Pappalardo; | arxiv-cs.LG | 2022-02-22 |
| 229 | Social Computational Design Method for Generating Product Shapes with GAN and Transformer Models Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: A social computational design method is established, aiming at taking advantages of the fast-developing artificial intelligence technologies for intelligent product design. |
Maolin Yang; Pingyu Jiang; | arxiv-cs.AI | 2022-02-22 |
| 230 | Synthetic CT Skull Generation for Transcranial MR Imaging-Guided Focused Ultrasound Interventions with Conditional Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We compared the performance of synthetic CT to real CT images using Kranion and k-Wave acoustic simulation. |
HAN LIU et. al. | arxiv-cs.CV | 2022-02-21 |
| 231 | Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we found that the recent emerging paradigm of implicit neural representations (INRs) that encodes a continuous signal into a parameterized neural network effectively mitigates the issue. |
SIHYUN YU et. al. | arxiv-cs.CV | 2022-02-21 |
| 232 | GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Past work that quantifies such uncertainty often makes simplifying assumptions on geometric variations, while the real-world, free-form uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a Generative Adversarial Network-based Design under Uncertainty Framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. |
Wei Wayne Chen; Doksoo Lee; Oluwaseyi Balogun; Wei Chen; | arxiv-cs.CE | 2022-02-21 |
| 233 | Transferring Adversarial Robustness Through Robust Representation Matching Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Robust Representation Matching (RRM), a low-cost method to transfer the robustness of an adversarially trained model to a new model being trained for the same task irrespective of architectural differences. |
Pratik Vaishnavi; Kevin Eykholt; Amir Rahmati; | arxiv-cs.LG | 2022-02-21 |
| 234 | Holistic Attention-Fusion Adversarial Network for Single Image Defogging Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Adversarial learning-based image defogging methods have been extensively studied in computer vision due to their remarkable performance. |
Wei Liu; Cheng Chen; Rui Jiang; Tao Lu; Zixiang Xiong; | arxiv-cs.CV | 2022-02-19 |
| 235 | Region-Based Semantic Factorization in GANs Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a highly efficient algorithm to factorize the latent semantics learned by GANs concerning an arbitrary image region. |
Jiapeng Zhu; Yujun Shen; Yinghao Xu; Deli Zhao; Qifeng Chen; | arxiv-cs.CV | 2022-02-19 |
| 236 | PerFED-GAN: Personalized Federated Learning Via Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a federated learning method based on co-training and generative adversarial networks(GANs) that allows each client to design its own model to participate in federated learning training independently without sharing any model architecture or parameter information with other clients or a center. |
Xingjian Cao; Gang Sun; Hongfang Yu; Mohsen Guizani; | arxiv-cs.LG | 2022-02-18 |
| 237 | Fast Online Inference for Nonlinear Contextual Bandit Based on Generative Adversarial Network Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a neural bandit model with an end-to-end training process to efficiently perform bandit algorithms such as Thompson Sampling and UCB during inference. |
Yun Da Tsai; Shou De Lin; | arxiv-cs.LG | 2022-02-17 |
| 238 | Point Cloud Generation with Continuous Conditioning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Generative models can be used to synthesize 3D objects of high quality and diversity. |
Larissa T. Triess; Andre Bühler; David Peter; Fabian B. Flohr; J. Marius Zöllner; | arxiv-cs.CV | 2022-02-17 |
| 239 | The Adversarial Security Mitigations of MmWave Beamforming Prediction Models Using Defensive Distillation and Adversarial Retraining Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents the security vulnerabilities in deep learning for beamforming prediction using deep neural networks (DNNs) in 6G wireless networks, which treats the beamforming prediction as a multi-output regression problem. |
Murat Kuzlu; Ferhat Ozgur Catak; Umit Cali; Evren Catak; Ozgur Guler; | arxiv-cs.CR | 2022-02-16 |
| 240 | Applying Adversarial Networks to Increase The Data Efficiency and Reliability of Self-Driving Cars Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The Adversarial Self-driving framework is applied to an image classification algorithm to improve the classification accuracy on perturbed images and is later applied to train a self-driving car to drive in a simulation. |
Aakash Kumar; | arxiv-cs.CV | 2022-02-15 |
| 241 | Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: With this in mind, this paper aims to detect intelligently designed illegitimate sensing service requests by integrating a GAN-based model. |
Zhiyan Chen; Burak Kantarci; | arxiv-cs.CR | 2022-02-15 |
| 242 | Deep Generative Model with Hierarchical Latent Factors for Time Series Anomaly Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents DGHL, a new family of generative models for time series anomaly detection, trained by maximizing the observed likelihood by posterior sampling and alternating back-propagation. |
Cristian Challu; Peihong Jiang; Ying Nian Wu; Laurent Callot; | arxiv-cs.LG | 2022-02-15 |
| 243 | Random Walks for Adversarial Meshes Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel, unified, and general adversarial attack, which leads to misclassification of numerous state-of-the-art mesh classification neural networks. |
Amir Belder; Gal Yefet; Ran Ben Izhak; Ayellet Tal; | arxiv-cs.CV | 2022-02-15 |
| 244 | GAN-generated Faces Detection: A Survey and New Perspectives (2022) Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim to provide a comprehensive review of recent progress in GAN-face detection. |
Xin Wang; Hui Guo; Shu Hu; Ming-Ching Chang; Siwei Lyu; | arxiv-cs.CV | 2022-02-14 |
| 245 | Artificial Intelligence-Based Smart Grid Vulnerabilities and Potential Solutions for Fake-Normal Attacks: A Short Review Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The purpose of this short review is to outline some of the initiatives to protect smart grid systems, their obstacles, and what might be a potential future AI research direction |
J. D. Ndibwile; | arxiv-cs.CR | 2022-02-14 |
| 246 | Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose a novel black-box adversarial attack method, namely Mixup-Attack, and its simple variant Mixcut-Attack, for remote sensing data. |
Yonghao Xu; Pedram Ghamisi; | arxiv-cs.CV | 2022-02-14 |
| 247 | Finding Dynamics Preserving Adversarial Winning Tickets Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Pruning methods have been considered in adversarial context to reduce model capacity and improve adversarial robustness simultaneously in training. |
Xupeng Shi; Pengfei Zheng; A. Adam Ding; Yuan Gao; Weizhong Zhang; | arxiv-cs.LG | 2022-02-14 |
| 248 | Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose UDA schemes using adversarial discriminative and generative methods for lane detection and classification applications in autonomous driving. |
CHUQING HU et. al. | arxiv-cs.CV | 2022-02-14 |
| 249 | Domain-Invariant Proposals Based on A Balanced Domain Classifier for Object Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we build domain-invariant detectors by learning domain classifiers via adversarial training. |
ZHIZE WU et. al. | arxiv-cs.CV | 2022-02-11 |
| 250 | Improving Image-recognition Edge Caches with A Generative Adversarial Network Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work shows that a well-known generative adversarial network, called ToDayGAN, can solve this problem by generating daytime images using nighttime ones. |
Guilherme B. Souza; Roberto G. Pacheco; Rodrigo S. Couto; | arxiv-cs.NI | 2022-02-11 |
| 251 | Open-set Adversarial Defense with Clean-Adversarial Mutual Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes an Open-Set Defense Network with Clean-Adversarial Mutual Learning (OSDN-CAML) as a solution to the OSAD problem. |
Rui Shao; Pramuditha Perera; Pong C. Yuen; Vishal M. Patel; | arxiv-cs.CV | 2022-02-11 |
| 252 | FAAG: Fast Adversarial Audio Generation Through Interactive Attack Optimisation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel scheme named FAAG as an iterative optimization-based method to generate targeted adversarial examples quickly. |
Yuantian Miao; Chao Chen; Lei Pan; Jun Zhang; Yang Xiang; | arxiv-cs.SD | 2022-02-10 |
| 253 | Gradient Methods Provably Converge to Non-Robust Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we identify natural settings where depth-$2$ ReLU networks trained with gradient flow are provably non-robust (susceptible to small adversarial $\ell_2$-perturbations), even when robust networks that classify the training dataset correctly exist. |
Gal Vardi; Gilad Yehudai; Ohad Shamir; | arxiv-cs.LG | 2022-02-09 |
| 254 | PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present PSA-GAN, a generative adversarial network (GAN) that generates long time series samples of high quality using progressive growing of GANs and self-attention. |
PAUL JEHA et. al. | iclr | 2022-02-08 |
| 255 | GATSBI: Generative Adversarial Training for Simulation-Based Inference Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Using generative adversarial networks for simulation-based inference |
POORNIMA RAMESH et. al. | iclr | 2022-02-08 |
| 256 | Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We demonstrate that Wasserstein GANs with two-layer discriminators and a variety of generators are equivalent to convex optimization problems or convex-concave games, allowing for global optimization in polynomial time and improved interpretability. |
ARDA SAHINER et. al. | iclr | 2022-02-08 |
| 257 | MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a differential-geometry-based technique to provably sample uniformly from the data manifold of a trained Deep Generative Network without the need for retraining. |
Ahmed Imtiaz Humayun; Randall Balestriero; Richard Baraniuk; | iclr | 2022-02-08 |
| 258 | ViTGAN: Training GANs with Vision Transformers IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Recently, Vision Transformers (ViTs) have shown competitive performance on image recognition while requiring less vision-specific inductive biases. In this paper, we investigate if such performance can be extended to image generation. |
KWONJOON LEE et. al. | iclr | 2022-02-08 |
| 259 | GDA-AM: ON THE EFFECTIVENESS OF SOLVING MIN-IMAX OPTIMIZATION VIA ANDERSON MIXING Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new minimax optimization framework,GDA-AM, that views the GDA dynamics as a fixed-point iteration and solves it using Anderson Mixing to converge to the local minimax. |
Huan He; Shifan Zhao; Yuanzhe Xi; Joyce Ho; Yousef Saad; | iclr | 2022-02-08 |
| 260 | Self-Conditioned Generative Adversarial Networks for Image Editing Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Building on this observation, we outline a method for mitigating generative bias through a self-conditioning process, where distances in the latent-space of a pre-trained generator are used to provide initial labels for the data. |
Yunzhe Liu; Rinon Gal; Amit H. Bermano; Baoquan Chen; Daniel Cohen-Or; | arxiv-cs.CV | 2022-02-08 |
| 261 | Implicit Bias of Adversarial Training for Deep Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide theoretical understandings of the implicit bias imposed by adversarial training for homogeneous deep neural networks without explicit regularization. |
Bochen Lv; Zhanxing Zhu; | iclr | 2022-02-08 |
| 262 | Adversarial Robustness Through The Lens of Causality Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The first attempt towards using causality to understand and mitigate adversarial vulnerability. |
YONGGANG ZHANG et. al. | iclr | 2022-02-08 |
| 263 | Reducing Excessive Margin to Achieve A Better Accuracy Vs. Robustness Trade-off Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we closely examine the changes induced in the decision boundary of a deep network during adversarial training. |
Rahul Rade; Seyed-Mohsen Moosavi-Dezfooli; | iclr | 2022-02-08 |
| 264 | Towards Understanding The Robustness Against Evasion Attack on Categorical Data Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper explores the characterization and certification of the robustness against evasion attack with categorical input. |
Hongyan Bao; Yufei Han; Yujun Zhou; Yun Shen; Xiangliang Zhang; | iclr | 2022-02-08 |
| 265 | Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting Across Cities Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, this paper aims to propose a novel transferable traffic forecasting framework: Domain Adversarial Spatial-Temporal Network (DASTNet). |
YIHONG TANG et. al. | arxiv-cs.LG | 2022-02-07 |
| 266 | TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: RNN-based GANs suffer from the fact that they cannot effectively model long sequences of data points with irregular temporal relations. To tackle these problems, we introduce TTS-GAN, a transformer-based GAN which can successfully generate realistic synthetic time-series data sequences of arbitrary length, similar to the real ones. |
Xiaomin Li; Vangelis Metsis; Huangyingrui Wang; Anne Hee Hiong Ngu; | arxiv-cs.LG | 2022-02-05 |
| 267 | Layer-wise Regularized Adversarial Training Using Layers Sustainability Analysis (LSA) Framework Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a novel framework (Layer Sustainability Analysis (LSA)) for the analysis of layer vulnerability in an arbitrary neural network in the scenario of adversarial attacks. |
Mohammad Khalooei; Mohammad Mehdi Homayounpour; Maryam Amirmazlaghani; | arxiv-cs.CV | 2022-02-05 |
| 268 | Adversarial Detector with Robust Classifier Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel adversarial detector, which consists of a robust classifier and a plain one, to highly detect adversarial examples. |
Takayuki Osakabe; Maungmaung Aprilpyone; Sayaka Shiota; Hitoshi Kiya; | arxiv-cs.CV | 2022-02-05 |
| 269 | GANSlider: How Users Control Generative Models for Images Using Multiple Sliders with and Without Feedforward Information Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate how multiple sliders with and without feedforward visualizations influence users’ control of generative models. |
Hai Dang; Lukas Mecke; Daniel Buschek; | arxiv-cs.HC | 2022-02-02 |
| 270 | Training A Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In our proposed method, we construct the trained encoder-discriminator as a one-class classifier based on Bidirectional GAN (Bi-GAN) for detecting anomalous traffic from normal traffic other than calculating expensive and complex anomaly scores or thresholds. |
Wen Xu; Julian Jang-Jaccard; Tong Liu; Fariza Sabrina; | arxiv-cs.LG | 2022-02-02 |
| 271 | Robust Estimation for Nonparametric Families Via Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In terms of techniques, our proposed GAN losses can be viewed as a smoothed and generalized Kolmogorov-Smirnov distance, which overcomes the computational intractability of the original Kolmogorov-Smirnov distance used in the prior work. |
Banghua Zhu; Jiantao Jiao; Michael I. Jordan; | arxiv-cs.LG | 2022-02-02 |
| 272 | Robust Binary Models By Pruning Randomly-initialized Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose ways to obtain robust models against adversarial attacks from randomly-initialized binary networks. |
Chen Liu; Ziqi Zhao; Sabine Süsstrunk; Mathieu Salzmann; | arxiv-cs.LG | 2022-02-02 |
| 273 | Adversarial Imitation Learning from Video Using A State Observer Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Towards addressing this issue, we introduce here a new algorithm called Visual Generative Adversarial Imitation from Observation using a State Observer VGAIfO-SO. |
Haresh Karnan; Garrett Warnell; Faraz Torabi; Peter Stone; | arxiv-cs.RO | 2022-02-01 |
| 274 | GADoT: GAN-based Adversarial Training for Robust DDoS Attack Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents an adversarial training approach called GADoT, which leverages a Generative Adversarial Network (GAN) to generate adversarial DDoS samples for training. |
Maged Abdelaty; Sandra Scott-Hayward; Roberto Doriguzzi-Corin; Domenico Siracusa; | arxiv-cs.CR | 2022-01-31 |
| 275 | Learning Robust Representation Through Graph Adversarial Contrastive Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To improve the robustness of graph representation learning, we propose a novel Graph Adversarial Contrastive Learning framework (GraphACL) by introducing adversarial augmentations into graph self-supervised learning. |
Jiayan Guo; Shangyang Li; Yue Zhao; Yan Zhang; | arxiv-cs.LG | 2022-01-31 |
| 276 | Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we evaluate the robustness of state-of-the-art image classification models trained on the MNIST and CIFAR10 datasets against the fast gradient sign method attack, a simple yet effective method of deceiving neural networks. |
Chandresh Pravin; Ivan Martino; Giuseppe Nicosia; Varun Ojha; | arxiv-cs.LG | 2022-01-31 |
| 277 | On The Robustness of Quality Measures for GANs Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work evaluates the robustness of quality measures of generative models such as Inception Score (IS) and Fr\’echet Inception Distance (FID). |
MOTASEM ALFARRA et. al. | arxiv-cs.LG | 2022-01-31 |
| 278 | Lorentzian Fully Hyperbolic Generative Adversarial Network Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a hyperbolic generative adversarial network (GAN) within the Lorentz model for generating hyperbolic data. |
Eric Qu; Dongmian Zou; | arxiv-cs.LG | 2022-01-30 |
| 279 | Improving Corruption and Adversarial Robustness By Enhancing Weak Subnets Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we observe that weak subnetwork (subnet) performance is correlated with a lack of robustness against corruptions and adversarial attacks. |
Yong Guo; David Stutz; Bernt Schiele; | arxiv-cs.CV | 2022-01-30 |
| 280 | Generative Cooperative Networks for Natural Language Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce Generative Cooperative Networks, in which the discriminator architecture is cooperatively used along with the generation policy to output samples of realistic texts for the task at hand. |
SYLVAIN LAMPRIER et. al. | arxiv-cs.LG | 2022-01-28 |
| 281 | Using Constant Learning Rate of Two Time-Scale Update Rule for Training Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we give a theoretical analysis of TTUR using constant learning rates to bridge the gap between theory and practice. |
Naoki Sato; Hideaki Iiduka; | arxiv-cs.LG | 2022-01-28 |
| 282 | Generative Adversarial Exploration for Reinforcement Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel method called generative adversarial exploration (GAEX) to encourage exploration in RL via introducing an intrinsic reward output from a generative adversarial network, where the generator provides fake samples of states that help discriminator identify those less frequently visited states. |
WEIJUN HONG et. al. | arxiv-cs.LG | 2022-01-27 |
| 283 | Generalised Image Outpainting with U-Transformer Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: While most present image outpainting conducts horizontal extrapolation, we study the generalised image outpainting problem that extrapolates visual context all-side around a given image. To this end, we develop a novel transformer-based generative adversarial network called U-Transformer able to extend image borders with plausible structure and details even for complicated scenery images. |
PENGLEI GAO et. al. | arxiv-cs.CV | 2022-01-27 |
| 284 | FinGAN: Generative Adversarial Network for Analytical Customer Relationship Management in Banking and Insurance Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In the first, we propose an oversampling method to generate synthetic samples of minority class using Generative Adversarial Network (GAN). |
Prateek Kate; Vadlamani Ravi; Akhilesh Gangwar; | arxiv-cs.CE | 2022-01-27 |
| 285 | Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, with only the knowledge of the ImageNet domain, we propose a Beyond ImageNet Attack (BIA) to investigate the transferability towards black-box domains (unknown classification tasks). |
QILONG ZHANG et. al. | arxiv-cs.CV | 2022-01-27 |
| 286 | Generative Trees: Adversarial and Copycat Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a new path forward for the generation of tabular data, exploiting decades-old understanding of the supervised task’s best components for DT induction, from losses (properness), models (tree-based) to algorithms (boosting). |
Richard Nock; Mathieu Guillame-Bert; | arxiv-cs.LG | 2022-01-26 |
| 287 | Boosting 3D Adversarial Attacks with Attacking On Frequency Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these problems, in this paper we propose a novel point cloud attack (dubbed AOF) that pays more attention on the low-frequency component of point clouds. |
Binbin Liu; Jinlai Zhang; Lyujie Chen; Jihong Zhu; | arxiv-cs.CV | 2022-01-26 |
| 288 | Sparsity Regularization For Cold-Start Recommendation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we introduce a novel representation for user-vectors by combining user demographics and user preferences, making the model a hybrid system which uses Collaborative Filtering and Content Based Recommendation. |
Aksheshkumar Ajaykumar Shah; Hemanth Venkateswara; | arxiv-cs.IR | 2022-01-25 |
| 289 | Image Generation with Self Pixel-wise Normalization Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To resolve this problem, this paper presents a novel normalization method, called self pixel-wise normalization (SPN), which effectively boosts the generative performance by performing the pixel-adaptive affine transformation without the mask image. |
Yoon-Jae Yeo; Min-Cheol Sagong; Seung Park; Sung-Jea Ko; Yong-Goo Shin; | arxiv-cs.CV | 2022-01-25 |
| 290 | GIU-GANs: Global Information Utilization for Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the aforementioned limitations, in this paper, we propose a new GANs called Involution Generative Adversarial Networks (GIU-GANs). |
YONGQI TIAN et. al. | arxiv-cs.LG | 2022-01-25 |
| 291 | Class-Aware Generative Adversarial Transformers for Medical Image Segmentation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present CASTformer, a novel type of generative adversarial transformers, for 2D medical image segmentation. |
CHENYU YOU et. al. | arxiv-cs.CV | 2022-01-25 |
| 292 | TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, therefore, we propose an infrared and visible image fusion algorithm based on a lightweight transformer module and adversarial learning. |
Dongyu Rao; Xiao-Jun Wu; Tianyang Xu; | arxiv-cs.CV | 2022-01-25 |
| 293 | Novel Blood Pressure Waveform Reconstruction from Photoplethysmography Using Cycle Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a cycle generative adversarial network (CycleGAN) based approach to extract a BP signal known as ambulatory blood pressure (ABP) from a clean PPG signal. |
Milad Asgari Mehrabadi; Seyed Amir Hossein Aqajari; Amir Hosein Afandizadeh Zargari; Nikil Dutt; Amir M. Rahmani; | arxiv-cs.LG | 2022-01-24 |
| 294 | Generative Adversarial Network Applications in Creating A Meta-Universe Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we discuss how GANs can be used to create an artificial world. |
Soheyla Amirian; Thiab R. Taha; Khaled Rasheed; Hamid R. Arabnia; | arxiv-cs.CV | 2022-01-22 |
| 295 | Explore The Expression: Facial Expression Generation Using Auxiliary Classifier Generative Adversarial Network Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this article, we propose a generative model architecture which robustly generates a set of facial expressions for multiple character identities and explores the possibilities of generating complex expressions by combining the simple ones. |
J. Rafid Siddiqui; | arxiv-cs.CV | 2022-01-22 |
| 296 | Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Façade Modeling Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate the work of data annotation for learned 3D modeling of fa\c{c}ades, this paper proposed a semi-supervised adversarial recognition strategy embedded in inverse procedural modeling. |
HAN HU et. al. | arxiv-cs.CV | 2022-01-22 |
| 297 | Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image Classification Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed an Adaptive DropBlock-enhanced Generative Adversarial Networks (ADGAN) for HSI classification. |
Junjie Wang; Feng Gao; Junyu Dong; Qian Du; | arxiv-cs.CV | 2022-01-21 |
| 298 | GAN-based Matrix Factorization for Recommender Systems Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we propose a new GAN-based approach that learns user and item latent factors in a matrix factorization setting for the generic top-N recommendation problem. |
Ervin Dervishaj; Paolo Cremonesi; | arxiv-cs.IR | 2022-01-20 |
| 299 | A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents a review and taxonomy based on solutions to the training problems of GANs in the biomedical imaging domain. |
Muhammad Muneeb Saad; Ruairi O’Reilly; Mubashir Husain Rehmani; | arxiv-cs.LG | 2022-01-19 |
| 300 | Simpler Is Better: Spectral Regularization and Up-sampling Techniques for Variational Autoencoders Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a simple 2D Fourier transform-based spectral regularization loss for the VAE and show that it can achieve results equal to, or better than, the current state-of-the-art in frequency-aware losses for generative models. |
Sara Björk; Jonas Nordhaug Myhre; Thomas Haugland Johansen; | arxiv-cs.LG | 2022-01-19 |
| 301 | Autoencoding Video Latents for Adversarial Video Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we propose to autoencode the motion and appearance latent vectors of the video generator in the adversarial setting. |
Sai Hemanth Kasaraneni; | arxiv-cs.CV | 2022-01-18 |
| 302 | Hardware-Efficient Deconvolution-Based GAN for Edge Computing Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed an HW/SW co-design approach for training quantized deconvolution GAN (QDCGAN) implemented on FPGA using a scalable streaming dataflow architecture capable of achieving higher throughput versus resource utilization trade-off. |
Azzam Alhussain; Mingjie Lin; | arxiv-cs.LG | 2022-01-18 |
| 303 | Contextual Road Lane and Symbol Generation for Autonomous Driving Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present a novel approach for lane detection and segmentation using generative models. |
Ajay Soni; Pratik Padamwar; Krishna Reddy Konda; | arxiv-cs.CV | 2022-01-18 |
| 304 | Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose a Variational AutoEncoder Generative Adversarial Network (VAE-GAN) as a smart grid data generative model which is capable of learning various types of data distributions and generating plausible samples from the same distribution without performing any prior analysis on the data before the training phase.We compared the Kullback-Leibler (KL) divergence, maximum mean discrepancy (MMD), and Wasserstein distance between the synthetic data (electrical load and PV production) distribution generated by the proposed model, vanilla GAN network, and the real data distribution, to evaluate the performance of our model. |
Mina Razghandi; Hao Zhou; Melike Erol-Kantarci; Damla Turgut; | arxiv-cs.LG | 2022-01-18 |
| 305 | Can We Find Neurons That Cause Unrealistic Images in Deep Generative Networks? Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, by analyzing the statistics and the roles for those neurons, we empirically show that rarely activated neurons are related to the failure results of making diverse objects and inducing artifacts. |
Hwanil Choi; Wonjoon Chang; Jaesik Choi; | arxiv-cs.CV | 2022-01-17 |
| 306 | Lifelong Generative Learning Via Knowledge Reconstruction Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we develop an efficient and effective lifelong generative model based on variational autoencoder (VAE). |
Libo Huang; Zhulin An; Xiang Zhi; Yongjun Xu; | arxiv-cs.LG | 2022-01-17 |
| 307 | SigGAN : Adversarial Model for Learning Signed Relationships in Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the recent success of Generative Adversarial Network (GAN) based models which comprises of a discriminator and generator in several applications, we propose a Generative Adversarial Network (GAN) based model for signed networks, SigGAN. |
Roshni Chakraborty; Ritwika Das; Joydeep Chandra; | arxiv-cs.SI | 2022-01-17 |
| 308 | Collapse By Conditioning: Training Class-conditional GANs with Limited Data Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by this observation, we propose a training strategy for class-conditional GANs (cGANs) that effectively prevents the observed mode-collapse by leveraging unconditional learning. |
Mohamad Shahbazi; Martin Danelljan; Danda Pani Paudel; Luc Van Gool; | arxiv-cs.CV | 2022-01-17 |
| 309 | Fully Convolutional Change Detection Framework with Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we proposed a fully convolutional change detection framework with generative adversarial network, to conclude unsupervised, weakly supervised, regional supervised, and fully supervised change detection tasks into one framework. |
Chen Wu; Bo Du; Liangpei Zhang; | arxiv-cs.CV | 2022-01-16 |
| 310 | ALA: Adversarial Lightness Attack Via Naturalness-aware Regularizations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To generate unrestricted adversarial examples with high image quality and good transferability, in this paper, we propose Adversarial Lightness Attack (ALA), a white-box unrestricted adversarial attack that focuses on modifying the lightness of the images. |
LIANGRU SUN et. al. | arxiv-cs.CV | 2022-01-16 |
| 311 | Arbitrary Handwriting Image Style Transfer Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposed a method to imitate handwriting style by style transfer. |
Kai Yang; Xiaoman Liang; Huihuang Zhao; | arxiv-cs.CV | 2022-01-14 |
| 312 | Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks (CAPGAN) as an augmentation tool to generate realistic synthetic images. |
YUCHONG YAO et. al. | arxiv-cs.CV | 2022-01-13 |
| 313 | The Effectiveness of Time Stretching for Enhancing Dysarthric Speech for Improved Dysarthric Speech Recognition Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate several existing and a new state-of-the-art generative adversarial network-based (GAN) voice conversion method for enhancing dysarthric speech for improved dysarthric speech recognition. |
Luke Prananta; Bence Mark Halpern; Siyuan Feng; Odette Scharenborg; | arxiv-cs.SD | 2022-01-13 |
| 314 | Data Augmentation Through Multivariate Scenario Forecasting in Data Centers Using Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a time-series data augmentation methodology based on synthetic scenario forecasting within the Data Center. |
Jaime Pérez; Patricia Arroba; José M. Moya; | arxiv-cs.LG | 2022-01-12 |
| 315 | Adversarially Robust Classification By Conditional Generative Model Inversion Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a classification model that does not obfuscate gradients and is robust by construction without assuming prior knowledge about the attack. |
Mitra Alirezaei; Tolga Tasdizen; | arxiv-cs.LG | 2022-01-12 |
| 316 | BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Through an extensive ablation study we show big gains in leveraging a large generated dataset to train different supervised and self-supervised backbone models on pixel-wise tasks. |
DAIQING LI et. al. | arxiv-cs.CV | 2022-01-12 |
| 317 | Deep Clustering with Fusion Autoencoder Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a novel DC method is proposed to address this issue. |
Shuai Chang; | arxiv-cs.LG | 2022-01-11 |
| 318 | FedDTG:Federated Data-Free Knowledge Distillation Via Three-Player Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a distributed three-player GAN to implement datafree co-distillation between clients. |
Zhenyuan Zhang; | arxiv-cs.LG | 2022-01-10 |
| 319 | Differentiable and Scalable Generative Adversarial Models for Data Imputation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an effective scalable imputation system named SCIS to significantly speed up the training of the differentiable generative adversarial imputation models under accuracy-guarantees for large-scale incomplete data. |
Yangyang Wu; Jun Wang; Xiaoye Miao; Wenjia Wang; Jianwei Yin; | arxiv-cs.LG | 2022-01-10 |
| 320 | Differentially Private Generative Adversarial Networks with Model Inversion Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Differentially Private Model Inversion (DPMI) method where the private data is first mapped to the latent space via a public generator, followed by a lower-dimensional DP-GAN with better convergent properties. |
Dongjie Chen; Sen-ching Samson Cheung; Chen-Nee Chuah; Sally Ozonoff; | arxiv-cs.LG | 2022-01-09 |
| 321 | Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we propose a novel framework for automated breaking of dark web CAPTCHA to facilitate dark web data collection. |
Ning Zhang; Mohammadreza Ebrahimi; Weifeng Li; Hsinchun Chen; | arxiv-cs.CV | 2022-01-08 |
| 322 | GenLabel: Mixup Relabeling Using Generative Models Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To resolve this, we propose GenLabel, a simple yet effective relabeling algorithm designed for mixup. |
JY-YONG SOHN et. al. | arxiv-cs.LG | 2022-01-07 |
| 323 | Realistic Full-Body Anonymization with Surface-Guided GANs Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new anonymization method that generates close-to-photorealistic humans for in-the-wild images.A key part of our design is to guide adversarial nets by dense pixel-to-surface correspondences between an image and a canonical 3D surface.We introduce Variational Surface-Adaptive Modulation (V-SAM) that embeds surface information throughout the generator.Combining this with our novel discriminator surface supervision loss, the generator can synthesize high quality humans with diverse appearance in complex and varying scenes.We show that surface guidance significantly improves image quality and diversity of samples, yielding a highly practical generator.Finally, we demonstrate that surface-guided anonymization preserves the usability of data for future computer vision development |
Håkon Hukkelås; Morten Smebye; Rudolf Mester; Frank Lindseth; | arxiv-cs.CV | 2022-01-06 |
| 324 | Biphasic Face Photo-Sketch Synthesis Via Semantic-Driven Generative Adversarial Network with Graph Representation Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel Semantic-Driven Generative Adversarial Network to address the above issues, cooperating with the Graph Representation Learning. |
Xingqun Qi; Muyi Sun; Qi Li; Caifeng Shan; | arxiv-cs.CV | 2022-01-05 |
| 325 | Culture-to-Culture Image Translation with Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This article introduces the concept of image culturization, i.e., defined as the process of altering the brushstroke of cultural features that make objects perceived as belonging to a given culture while preserving their functionalities. |
Giulia Zaino; Carmine Tommaso Recchiuto; Antonio Sgorbissa; | arxiv-cs.CV | 2022-01-05 |
| 326 | Corrupting Data to Remove Deceptive Perturbation: Using Preprocessing Method to Improve System Robustness Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a new approach to improve neural network robustness by applying the recovery process on top of the naturally trained classifier. |
Hieu Le; Hans Walker; Dung Tran; Peter Chin; | arxiv-cs.CV | 2022-01-04 |
| 327 | Towards Understanding and Harnessing The Effect of Image Transformation in Adversarial Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we systematically synthesize the recent progress on adversarial detection via image transformations with a novel classification method. |
Hui Liu; Bo Zhao; Yuefeng Peng; Weidong Li; Peng Liu; | arxiv-cs.CV | 2022-01-04 |
| 328 | Neural Network Training Under Semidefinite Constraints Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper is concerned with the training of neural networks (NNs) under semidefinite constraints, which allows for NN training with robustness and stability guarantees. |
Patricia Pauli; Niklas Funcke; Dennis Gramlich; Mohamed Amine Msalmi; Frank Allgöwer; | arxiv-cs.LG | 2022-01-03 |
| 329 | Actor-Critic Network for Q&A in An Adversarial Environment Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces an approach that joins these two ideas together to train a critic model for use in an almost reinforcement learning framework. |
Bejan Sadeghian; | arxiv-cs.CL | 2022-01-02 |
| 330 | Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: The novel Coronavirus disease 2019 (COVID-19) pandemic has begun in China and is still affecting thousands of patient lives worldwide daily. Although Chest X-ray and Computed … |
Mohamed Esmail Karar; Marwa Ahmed Shouman; Claire Chalopin; | Computers, Materials & Continua | 2022-01-01 |
| 331 | A Deep Framework for Enhancement of Diagnostic Information in CSMRI Reconstruction Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Abstract In compressed sensing (CS)-based magnetic resonance imaging (MRI), it is very challenging to maintain the diagnostic quality due to limited measurements. Diagnostically … |
Sumit Datta; Samarendra Dandapat; Bhabesh Deka; | Biomed. Signal Process. Control. | 2022-01-01 |
| 332 | Cancer Classification with Data Augmentation Based on Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details |
Kaimin Wei; Tianqi Li; Feiran Huang; Jinpeng Chen; Zefan He; | Frontiers Comput. Sci. | 2022-01-01 |
| 333 | Behind The Leaves: Estimation of Occluded Grapevine Berries With Conditional Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: The need for accurate yield estimates for viticulture is becoming more important due to increasing competition in the wine market worldwide. One of the most promising methods to … |
JANA KIERDORF et. al. | 2022-01-01 | |
| 334 | Stochastic Simulation of Fan Deltas Using Parallel Multi-stage Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Abstract The stochastic simulation of fan deltas has always been one of the significant problems in the numerical simulation of reservoirs. As one of the important numerical … |
Ting Zhang; Zhonghao Yang; Chaochao Sun; | Journal of Petroleum Science and Engineering | 2022-01-01 |
| 335 | Potential Flow Generator With L2 Optimal Transport Regularity for Generative Models IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: We propose a potential flow generator with |
Liu Yang; George Em Karniadakis; | IEEE Transactions on Neural Networks and Learning Systems | 2022-01-01 |
| 336 | Adversarial Autoencoder Based Feature Learning for Fault Detection in Industrial Processes Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Deep learning has recently emerged as a promising method for nonlinear process monitoring. However, ensuring that the features from process variables have representative … |
Kyojin Jang; Seokyoung Hong; Minsu Kim; Jonggeol Na; Il Moon; | IEEE Transactions on Industrial Informatics | 2022-01-01 |
| 337 | Combining Residual Attention Mechanisms and Generative Adversarial Networks for Hippocampus Segmentation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: This research discussed a deep learning method based on an improved generative adversarial network to segment the hippocampus. Different convolutional configurations were proposed … |
HONGXIA DENG et. al. | Tsinghua Science & Technology | 2022-01-01 |
| 338 | Wind Farm Wake Modeling Based on Deep Convolutional Conditional Generative Adversarial Network Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Abstract Modeling of wind farm wakes is of great importance for the optimal design and operation of wind farms. In this work a surrogate modeling method for parametrized fluid … |
Jincheng Zhang; Xiaowei Zhao; | Energy | 2022-01-01 |
| 339 | Development of A Biofeedback System Using Harmonic Musical Intervals to Control Heart Rate Variability with A Generative Adversarial Network Literature Review Related Patents Related Grants Related Orgs Related Experts Details |
Ennio Idrobo-Ávila; Humberto Loaiza-Correa; Flavio Muñoz-Bolaños; Leon van Noorden; Rubiel Vargas-Cañas; | Biomed. Signal Process. Control. | 2022-01-01 |
| 340 | A New 3D MRI Segmentation Method Based on Generative Adversarial Network and Atrous Convolution Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Abstract Background and aim Although many algorithms have been proposed to segment brain structures in MRI scans, comparison of different algorithms in the same data set is rarely … |
Gaffari Çelik; Muhammed Fatih Talu; | Biomed. Signal Process. Control. | 2022-01-01 |
| 341 | GAN-based Spatial Image Steganography with Cross Feedback Mechanism Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Abstract Steganography has been widely used to realize covert communication with multimedia content. By simulating the competition between a generator and a discriminator in a … |
Fengyong Li; Zongliang Yu; Chuan Qin; | Signal Process. | 2022-01-01 |
| 342 | Adversarial Machine Learning: A Multilayer Review of The State-of-the-Art and Challenges for Wireless and Mobile Systems Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Machine Learning (ML) models are susceptible to adversarial samples that appear as normal samples but have some imperceptible noise added to them with the intention of misleading … |
Jinxin Liu; Michele Nogueira; Johan Fernandes; Burak Kantarci; | IEEE Communications Surveys & Tutorials | 2022-01-01 |
| 343 | Generative Adversarial Networks for Speech Processing: A Review Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Abstract Generative adversarial networks (GANs) have seen remarkable progress in recent years. They are used as generative models for all kinds of data such as text, images, … |
AAMIR WALI et. al. | Computer Speech & Language | 2022-01-01 |
| 344 | A Proxy Model to Predict Reservoir Dynamic Pressure Profile of Fracture Network Based on Deep Convolutional Generative Adversarial Networks (DCGAN) Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Abstract Tight oil and gas reservoirs with huge development potential are widely distributed all over the world and horizontal well technique is a popular development technology … |
XIAOYIN PENG et. al. | Journal of Petroleum Science and Engineering | 2022-01-01 |
| 345 | SLC-GAN: An Automated Myocardial Infarction Detection Model Based on Generative Adversarial Networks and Convolutional Neural Networks with Single-lead Electrocardiogram Synthesis Literature Review Related Patents Related Grants Related Orgs Related Experts Details |
Wenqiang Li; Yuk-Ming Tang; Kai Ming Yu; Suet To; | Inf. Sci. | 2022-01-01 |
| 346 | Prognostics With Variational Autoencoder By Generative Adversarial Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Prognostics predicts the future performance progression and remaining useful life (RUL) of in-service systems based on historical and contemporary data. One of the challenges in … |
Yu Huang; Yufei Tang; James H. VanZwieten; | IEEE Transactions on Industrial Electronics | 2022-01-01 |
| 347 | Adversarial Perturbation Defense on Deep Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Deep neural networks (DNNs) have been verified to be easily attacked by well-designed adversarial perturbations. Image objects with small perturbations that are imperceptible to … |
Xingwei Zhang; Xiaolong Zheng; Wenji Mao; | ACM Computing Surveys (CSUR) | 2022-01-01 |
| 348 | Adversarial Detection By Latent Style Transformations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Detection-based defense approaches are effective against adversarial attacks without compromising the structure of the protected model. However, they could be bypassed by stronger … |
Shuo Wang; Surya Nepal; Alsharif Abuadbba; Carsten Rudolph; Marthie Grobler; | IEEE Transactions on Information Forensics and Security | 2022-01-01 |
| 349 | Deep Image Prior Based Defense Against Adversarial Examples Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Abstract Recently, deep neural networks (DNNs) have shown serious vulnerability to adversarial examples with imperceptible perturbation to clean images. To counter this issue, … |
Tao Dai; Yan Feng; Bin Chen; Jian Lu; Shu-Tao Xia; | Pattern Recognition | 2022-01-01 |
| 350 | on The Effectiveness of Generative Adversarial Network on Anomaly Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Following this analogy, we suggested a new unsupervised model based on GANs –a combination of an autoencoder and a GAN. |
Laya Rafiee Sevyeri; Thomas Fevens; | arxiv-cs.LG | 2021-12-31 |
| 351 | Adversarial Attack Via Dual-Stage Network Erosion Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, this paper proposes to improve the transferability of adversarial examples, and applies dual-stage feature-level perturbations to an existing model to implicitly create a set of diverse models. |
YEXIN DUAN et. al. | arxiv-cs.CV | 2021-12-31 |
| 352 | SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we present a 3D shape synthesis framework (SurfGen) that directly applies adversarial training to the object surface. |
Andrew Luo; Tianqin Li; Wen-Hao Zhang; Tai Sing Lee; | arxiv-cs.CV | 2021-12-31 |
| 353 | Conditional Generative Data-free Knowledge Distillation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a conditional generative data-free knowledge distillation (CGDD) framework for training lightweight networks without any training data. |
Xinyi Yu; Ling Yan; Yang Yang; Libo Zhou; Linlin Ou; | arxiv-cs.CV | 2021-12-31 |
| 354 | Deep Adversarial Network Alignment IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, we present our Deep Adversarial Network Alignment (DANA) framework that first uses deep adversarial learning to discover complex mappings for aligning the embedding distributions of the two networks. |
Tyler Derr; Hamid Karimi; Xiaorui Liu; Jiejun Xu; Jiliang Tang; | cikm | 2021-12-30 |
| 355 | Exploratory Search of GANs with Contextual Bandits Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this article, we present a simulation study exploring the performance of Gaussian Process bandits in the context of interactive GAN exploration. |
Ivan Kropotov; Alan Medlar; Dorota Glowacka; | cikm | 2021-12-30 |
| 356 | Causal-Aware Generative Imputation for Automated Underwriting Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, rather than choosing off-the-shelf solutions tackling the complex data missing problem, we propose an innovative Generative Adversarial Nets (GAN) framework that can capture the missing pattern from a causal perspective. |
QIAN LI et. al. | cikm | 2021-12-30 |
| 357 | Detecting The Fake Candidate Instances: Ambiguous Label Learning with Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We formulate a unified adversarial objective with respect to three players, i.e., a discriminator, a generator, and a classifier. |
Changchun Li; Ximing Li; Jihong Ouyang; Yiming Wang; | cikm | 2021-12-30 |
| 358 | Adversarial Separation Network for Cross-Network Node Classification Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the above problems, in this paper, we propose a novel model, Adversarial Separation Network(ASN), to learn effective node representations between source and target networks. |
Xiaowen Zhang; Yuntao Du; Rongbiao Xie; Chongjun Wang; | cikm | 2021-12-30 |
| 359 | Training Neural Networks with Random Noise Images for Adversarial Robustness Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose RanTrain, a simple training approach which employs a background class with random noise images to augment the original DNN model and training data, without requiring any adversarial examples. |
Ji-Young Park; Lin Liu; Jiuyong Li; Jixue Liu; | cikm | 2021-12-30 |
| 360 | Graph Representation Learning Via Adversarial Variational Bayes Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, inspired by Adversarial Variational Bayes (AVB) [22], we propose GraphAVB, a probabilistic generative model to learn node representations that preserve connectivity patterns and capture the uncertainties in the graph. |
Yunhe Li; Yaochen Hu; Yingxue Zhang; | cikm | 2021-12-30 |
| 361 | Using Topic Modeling and Adversarial Neural Networks for Fake News Video Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper,we propose a topic-agnostic fake news video detection model based on adversarial learning and topic modeling. |
Hyewon Choi; Youngjoong Ko; | cikm | 2021-12-30 |
| 362 | Overcoming Mode Collapse with Adaptive Multi Adversarial Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by this observation, we introduce a novel training procedure that adaptively spawns additional discriminators to remember previous modes of generation. |
Karttikeya Mangalam; Rohin Garg; | arxiv-cs.CV | 2021-12-29 |
| 363 | FRIDA — Generative Feature Replay for Incremental Domain Adaptation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We tackle the novel problem of incremental unsupervised domain adaptation (IDA) in this paper. |
SAYAN RAKSHIT et. al. | arxiv-cs.CV | 2021-12-28 |
| 364 | Adversarial Attack for Asynchronous Event-based Data Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our algorithm achieves an attack success rate of 97.95\% on the N-Caltech101 dataset. |
Wooju Lee; Hyun Myung; | arxiv-cs.CV | 2021-12-27 |
| 365 | Perlin Noise Improve Adversarial Robustness Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Combined with the defensive idea of adversarial training, we use Perlin noise to train the neural network to obtain a model that can defend against procedural noise adversarial examples. |
Chengjun Tang; Kun Zhang; Chunfang Xing; Yong Ding; Zengmin Xu; | arxiv-cs.LG | 2021-12-26 |
| 366 | Comparison and Analysis of Image-to-Image Generative Adversarial Networks: A Survey Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we survey and analyze eight Image-to-Image Generative Adversarial Networks: Pix2Px, CycleGAN, CoGAN, StarGAN, MUNIT, StarGAN2, DA-GAN, and Self Attention GAN. |
Sagar Saxena; Mohammad Nayeem Teli; | arxiv-cs.CV | 2021-12-23 |
| 367 | Catch Me If You GAN: Using Artificial Intelligence for Fake Log Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: More precisely, three different generative adversarial networks, SeqGAN, MaliGAN, and CoT, are reviewed in this research regarding their performance, focusing on generating new logs as a means of deceiving system admins for red teams. |
Christian Toemmel; | arxiv-cs.CR | 2021-12-22 |
| 368 | Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a generative adversarial network (GAN) entitled EHR-M-GAN which synthesizes \textit{mixed-type} timeseries EHR data. |
Jin Li; Benjamin J. Cairns; Jingsong Li; Tingting Zhu; | arxiv-cs.LG | 2021-12-22 |
| 369 | Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the transferability of adversarial network traffic against multiple machine learning-based intrusion detection systems. |
Islam Debicha; Thibault Debatty; Jean-Michel Dricot; Wim Mees; Tayeb Kenaza; | arxiv-cs.CR | 2021-12-22 |
| 370 | Multiple Imputation Via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose Multiple Imputation via Generative Adversarial Network (MI-GAN), a deep learning-based (in specific, a GAN-based) multiple imputation method, that can work under missing at random (MAR) mechanism with theoretical support. |
Zongyu Dai; Zhiqi Bu; Qi Long; | arxiv-cs.LG | 2021-12-21 |
| 371 | A Theoretical View of Linear Backpropagation and Its Convergence Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper serves as a complement and somewhat an extension to Guo et al.’s paper, by providing theoretical analyses on LinBP in neural-network-involved learning tasks including adversarial attack and model training. |
Ziang Li; Yiwen Guo; Haodi Liu; Changshui Zhang; | arxiv-cs.LG | 2021-12-21 |
| 372 | Turbo-Sim: A Generalised Generative Model with A Physical Latent Space Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Turbo-Sim, a generalised autoencoder framework derived from principles of information theory that can be used as a generative model. |
Guillaume Quétant; Mariia Drozdova; Vitaliy Kinakh; Tobias Golling; Slava Voloshynovskiy; | arxiv-cs.LG | 2021-12-20 |
| 373 | Multimodal Adversarially Learned Inference with Factorized Discriminators Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel approach to generative modeling of multimodal data based on generative adversarial networks. |
Wenxue Chen; Jianke Zhu; | arxiv-cs.LG | 2021-12-20 |
| 374 | FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a framework, FedNI, to leverage network inpainting and inter-institutional data via FL. |
Liang Peng; Nan Wang; Nicha Dvornek; Xiaofeng Zhu; Xiaoxiao Li; | arxiv-cs.LG | 2021-12-19 |
| 375 | Initiative Defense Against Facial Manipulation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the limitation, in this paper, we propose a novel framework of initiative defense to degrade the performance of facial manipulation models controlled by malicious users. |
Qidong Huang; Jie Zhang; Wenbo Zhou; WeimingZhang; Nenghai Yu; | arxiv-cs.CV | 2021-12-19 |
| 376 | Investigation of Densely Connected Convolutional Networks with Domain Adversarial Learning for Noise Robust Speech Recognition Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate densely connected convolutional networks (DenseNets) and their extension with domain adversarial training for noise robust speech recognition. |
Chia Yu Li; Ngoc Thang Vu; | arxiv-cs.CL | 2021-12-19 |
| 377 | Generation of Data on Discontinuous Manifolds Via Continuous Stochastic Non-invertible Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, instead of trying to keep the latent space to be Gaussian, we use a pre-trained contrastive encoder to obtain a clustered latent space. |
Mariia Drozdova; Vitaliy Kinakh; Guillaume Quétant; Tobias Golling; Slava Voloshynovskiy; | arxiv-cs.LG | 2021-12-17 |
| 378 | Information-theoretic Stochastic Contrastive Conditional GAN: InfoSCC-GAN Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a stochastic contrastive conditional generative adversarial network (InfoSCC-GAN) with an explorable latent space. |
Vitaliy Kinakh; Mariia Drozdova; Guillaume Quétant; Tobias Golling; Slava Voloshynovskiy; | arxiv-cs.CV | 2021-12-17 |
| 379 | NFTGAN: Non-Fungible Token Art Generation Using Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a GAN-based architecture is implemented and evaluated for novel NFT-style digital arts generation. |
Sakib Shahriar; Kadhim Hayawi; | arxiv-cs.LG | 2021-12-17 |
| 380 | Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the dynamics-aware adversarial attack problem in deep neural networks. |
AN TAO et. al. | arxiv-cs.CV | 2021-12-17 |
| 381 | An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose the concept of local activation, and devise a metric on the local activation to detect artifact generations without additional supervision. |
Haedong Jeong; Jiyeon Han; Jaesik Choi; | arxiv-cs.CV | 2021-12-16 |
| 382 | Imbalanced Sample Generation and Evaluation for Power System Transient Stability Using CTGAN Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a controllable sample generation framework based on Conditional Tabular Generative Adversarial Network (CTGAN) to generate specified transient stability samples. |
GENGSHI HAN et. al. | arxiv-cs.LG | 2021-12-16 |
| 383 | Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Later, we apply the iterative adversarial finetuning to refine pretrained models. |
Tong Chen; Zhan Ma; | arxiv-cs.CV | 2021-12-16 |
| 384 | Models in The Loop: Aiding Crowdworkers with Generative Annotation Assistants Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we examine whether we can maintain the advantages of DADC, without incurring the additional cost. |
MAX BARTOLO et. al. | arxiv-cs.CL | 2021-12-16 |
| 385 | A Robust Optimization Approach to Deep Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new, more principled approach to adversarial training based on a closed form solution of an upper bound of the adversarial loss, which can be effectively trained with backpropagation. |
Dimitris Bertsimas; Xavier Boix; Kimberly Villalobos Carballo; Dick den Hertog; | arxiv-cs.LG | 2021-12-16 |
| 386 | Deep Generative Models for Geometric Design Under Uncertainty Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a Generative Adversarial Network-based Design under Uncertainty Framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. |
Wei Wayne Chen; Doksoo Lee; Wei Chen; | arxiv-cs.LG | 2021-12-15 |
| 387 | Leveraging Image-based Generative Adversarial Networks for Time Series Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The paper proposes the intertemporal return plot (IRP) representation to facilitate the use of image-based generative adversarial networks for time series generation. |
Justin Hellermann; Stefan Lessmann; | arxiv-cs.LG | 2021-12-15 |
| 388 | Self-Ensembling GAN for Cross-Domain Semantic Segmentation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mitigate the annotation burden, this paper proposes a self-ensembling generative adversarial network (SE-GAN) exploiting cross-domain data for semantic segmentation. |
Yonghao Xu; Fengxiang He; Bo Du; Liangpei Zhang; Dacheng Tao; | arxiv-cs.CV | 2021-12-15 |
| 389 | On The Convergence and Robustness of Adversarial Training IF:4 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: A criterion that measures how well the inner maximization is solved is therefore crucial for adversarial training. In this paper, we propose such a criterion, namely First-Order Stationary Condition for constrained optimization (FOSC), to quantitatively evaluate the convergence quality of adversarial examples found in the inner maximization. |
YISEN WANG et. al. | arxiv-cs.LG | 2021-12-15 |
| 390 | Tackling The Generative Learning Trilemma with Denoising Diffusion GANs Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Particularly, denoising diffusion models have shown impressive sample quality and diversity, but their expensive sampling does not yet allow them to be applied in many real-world applications. In this paper, we argue that slow sampling in these models is fundamentally attributed to the Gaussian assumption in the denoising step which is justified only for small step sizes. |
Zhisheng Xiao; Karsten Kreis; Arash Vahdat; | arxiv-cs.LG | 2021-12-14 |
| 391 | GM Score: Incorporating Inter-class and Intra-class Generator Diversity, Discriminability of Disentangled Representation, and Sample Fidelity for Evaluating GANs Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new score, namely, GM Score, which takes into various factors such as sample quality, disentangled representation, intra-class and inter-class diversity, and other metrics such as precision, recall, and F1 score are employed for discriminability of latent space of deep belief network (DBN) and restricted Boltzmann machine (RBM). |
Harshvardhan GM; Aanchal Sahu; Mahendra Kumar Gourisaria; | arxiv-cs.LG | 2021-12-13 |
| 392 | MAGIC: Multimodal RelAtional Graph AdversarIal InferenCe for Diverse and Unpaired Text-based Image Captioning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the Multimodal relAtional Graph adversarIal inferenCe (MAGIC) framework for diverse and unpaired TextCap. |
WENQIAO ZHANG et. al. | arxiv-cs.CV | 2021-12-13 |
| 393 | SmartCon: Deep Probabilistic Learning Based Intelligent Link-Configuration in Narrowband-IoT Towards 5G and B5G Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Accordingly, in this paper, we propose SmartCon which is a Generative Adversarial Network (GAN)-based deep learning approach for auto link-configuration during uplink or downlink scheduling, such that the packet loss rate is significantly reduced in NB-IoT networks. |
Raja Karmakar; Georges Kaddoum; Samiran Chattopadhyay; | arxiv-cs.NI | 2021-12-09 |
| 394 | Multimodal Conditional Image Synthesis with Product-of-Experts GANs Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this limitation, we propose the Product-of-Experts Generative Adversarial Networks (PoE-GAN) framework, which can synthesize images conditioned on multiple input modalities or any subset of them, even the empty set. |
Xun Huang; Arun Mallya; Ting-Chun Wang; Ming-Yu Liu; | arxiv-cs.CV | 2021-12-09 |
| 395 | Uncertainty, Edge, and Reverse-Attention Guided Generative Adversarial Network for Automatic Building Detection in Remotely Sensed Images Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome these problems, we propose a generative adversarial network based segmentation framework with uncertainty attention unit and refinement module embedded in the generator. |
Somrita Chattopadhyay; Avinash C. Kak; | arxiv-cs.CV | 2021-12-09 |
| 396 | Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a theoretical and computational framework for robustness verification of implicit neural networks; our framework blends together mixed monotone systems theory and contraction theory. |
Saber Jafarpour; Matthew Abate; Alexander Davydov; Francesco Bullo; Samuel Coogan; | arxiv-cs.LG | 2021-12-09 |
| 397 | Multiple Residual Dense Networks for Reconfigurable Intelligent Surfaces Cascaded Channel Estimation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Abstract: Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive … |
YU JIN et. al. | arxiv-cs.IT | 2021-12-08 |
| 398 | A Unified Architecture of Semantic Segmentation and Hierarchical Generative Adversarial Networks for Expression Manipulation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To demonstrate its advantages, we evaluate our method on two challenging facial expression translation benchmarks, AffectNet and RaFD, and a semantic segmentation benchmark, CelebAMask-HQ across two popular architectures, BiSeNet and UNet. |
Rumeysa Bodur; Binod Bhattarai; Tae-Kyun Kim; | arxiv-cs.CV | 2021-12-08 |
| 399 | On Visual Self-supervision and Its Effect on Model Robustness Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We identify primary ways in which self-supervision can be added to adversarial training, and observe that using a self-supervised loss to optimize both network parameters and find adversarial examples leads to the strongest improvement in model robustness, as this can be viewed as a form of ensemble adversarial training. |
Michal Kucer; Diane Oyen; Garrett Kenyon; | arxiv-cs.CV | 2021-12-08 |
| 400 | SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper therefore aims to comprehensively investigate the adversarial robustness of NLVL models by examining three facets of vulnerabilities from both attack and defense aspects. |
WENBO GOU et. al. | arxiv-cs.CV | 2021-12-08 |
| 401 | Permutation Equivariant Generative Adversarial Networks for Graphs Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: After having discussed some properties of such functions, we propose 3G-GAN, a 3-stages model relying on GANs and equivariant functions. |
Yoann Boget; Magda Gregorova; Alexandros Kalousis; | arxiv-cs.LG | 2021-12-07 |
| 402 | Generative Adversarial Networks for Labeled Data Creation for Structural Monitoring and Damage Detection Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The objective of this study is to address the data scarcity problem for damage detection. |
Furkan Luleci; F. Necati Catbas; Onur Avci; | arxiv-cs.LG | 2021-12-06 |
| 403 | Generative Adversarial Networks for Data Generation in Structural Health Monitoring Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, 1-D Wasserstein loss Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) is utilized to generate damage associated vibration datasets that are similar to the input. |
Furkan Luleci; F. Necati Catbas; Onur Avci; | arxiv-cs.LG | 2021-12-06 |
| 404 | Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Due to their massive success in various domains, deep learning techniques are increasingly used to design network intrusion detection solutions that detect and mitigate unknown and known attacks with high accuracy detection rates and minimal feature engineering. |
Huda Ali Alatwi; Charles Morisset; | arxiv-cs.CR | 2021-12-06 |
| 405 | BDFA: A Blind Data Adversarial Bit-flip Attack on Deep Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Blind Data Adversarial Bit-flip Attack (BDFA), a novel technique to enable BFA without any access to the training or testing data. |
Behnam Ghavami; Mani Sadati; Mohammad Shahidzadeh; Zhenman Fang; Lesley Shannon; | arxiv-cs.CR | 2021-12-06 |
| 406 | Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work explores the potency of stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA), against powerful (gradient-based) white-box and black-box adversarial attacks; we especially focus on Adversarial Training settings. |
Konstantinos P. Panousis; Sotirios Chatzis; Sergios Theodoridis; | arxiv-cs.LG | 2021-12-05 |
| 407 | Construct Informative Triplet with Two-stage Hard-sample Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a robust sample generation scheme to construct informative triplets. |
CHUANG ZHU et. al. | arxiv-cs.CV | 2021-12-04 |
| 408 | Implicit Data Augmentation Using Feature Interpolation for Low-Shot Image Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mitigate this issue, we propose a novel implicit data augmentation approach which facilitates stable training and synthesize high-quality samples without need of label information. |
Mengyu Dai; Haibin Hang; Xiaoyang Guo; | arxiv-cs.CV | 2021-12-04 |
| 409 | Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we consider the potential application of GANs for the purpose of generating synthetic census microdata. |
Claire Little; Mark Elliot; Richard Allmendinger; Sahel Shariati Samani; | arxiv-cs.LG | 2021-12-03 |
| 410 | Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we show that a novel DL-based causal language model enables single-shot evasion (i.e., with only one query to malware detector) by treating the content of the malware executable as a byte sequence and training a Generative Pre-Trained Transformer (GPT). |
James Lee Hu; Mohammadreza Ebrahimi; Hsinchun Chen; | arxiv-cs.CR | 2021-12-03 |
| 411 | Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks? Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Towards this, we present an extensive adversarial robustness analysis of different approximate DNN accelerators (AxDNNs) using the state-of-the-art approximate multipliers. |
Ayesha Siddique; Khaza Anuarul Hoque; | arxiv-cs.LG | 2021-12-02 |
| 412 | FaceTuneGAN: Face Autoencoder for Convolutional Expression Transfer Using Neural Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present FaceTuneGAN, a new 3D face model representation decomposing and encoding separately facial identity and facial expression. |
Nicolas Olivier; Kelian Baert; Fabien Danieau; Franck Multon; Quentin Avril; | arxiv-cs.CV | 2021-12-01 |
| 413 | $\ell_\infty$-Robustness and Beyond: Unleashing Efficient Adversarial Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, by leveraging the theory of coreset selection we show how selecting a small subset of training data provides a more principled approach towards reducing the time complexity of robust training. |
Hadi M. Dolatabadi; Sarah Erfani; Christopher Leckie; | arxiv-cs.LG | 2021-12-01 |
| 414 | Robustness in Deep Learning for Computer Vision: Mind The Gap? Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To provide a more transparent definition of robustness across contexts, we introduce a structural causal model of the data generating process and interpret non-adversarial robustness as pertaining to a model’s behavior on corrupted images which correspond to low-probability samples from the unaltered data distribution. |
Nathan Drenkow; Numair Sani; Ilya Shpitser; Mathias Unberath; | arxiv-cs.CV | 2021-12-01 |
| 415 | Adversarial Attacks Against Deep Generative Models on Data: A Survey Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: For each model, component and attack, we review the current research progress and identify the key challenges. |
Hui Sun; Tianqing Zhu; Zhiqiu Zhang; Dawei Jin. Ping Xiong; Wanlei Zhou; | arxiv-cs.CR | 2021-11-30 |
| 416 | Generative Convolution Layer for Image Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a novel convolution method, called generative convolution (GConv), which is simple yet effective for improving the generative adversarial network (GAN) performance. |
Seung Park; Yong-Goo Shin; | arxiv-cs.CV | 2021-11-30 |
| 417 | Improvement in Machine Translation with Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore machine translation improvement via Generative Adversarial Network (GAN) architecture. |
Jay Ahn; Hari Madhu; Viet Nguyen; | arxiv-cs.CL | 2021-11-30 |
| 418 | FMD-cGAN: Fast Motion Deblurring Using Conditional Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a Fast Motion Deblurring-Conditional Generative Adversarial Network (FMD-cGAN) that helps in blind motion deblurring of a single image. |
Jatin Kumar; Indra Deep Mastan; Shanmuganathan Raman; | arxiv-cs.CV | 2021-11-30 |
| 419 | Using A GAN to Generate Adversarial Examples to Facial Image Recognition Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we use a Generative Adversarial Network (GAN) to create adversarial examples to deceive facial recognition and we achieve an acceptable success rate in fooling the face recognition. |
Andrew Merrigan; Alan F. Smeaton; | arxiv-cs.CV | 2021-11-30 |
| 420 | Molecular Attributes Transfer from Non-Parallel Data Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, for the first time, we formulate molecular optimization as a style transfer problem and present a novel generative model that could automatically learn internal differences between two groups of non-parallel data through adversarial training strategies. |
SHUANGJIA ZHENG et. al. | arxiv-cs.LG | 2021-11-30 |
| 421 | Weakly-supervised Generative Adversarial Networks for Medical Image Classification Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel medical image classification algorithm, called Weakly-Supervised Generative Adversarial Networks (WSGAN), which only uses a small number of real images without labels to generate fake images or mask images to enlarge the sample size of the training set. |
Jiawei Mao; Xuesong Yin; Yuanqi Chang; Qi Huang; | arxiv-cs.CV | 2021-11-29 |
| 422 | Energy-Efficient Implementation of Generative Adversarial Networks on Passive RRAM Crossbar Arrays Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, there is an urgent need for development of ultra-compact and energy-efficient hardware accelerators for GANs. To this end, in this work, we propose to exploit the passive RRAM crossbar arrays for performing key operations of a fully-connected GAN: (a) true random noise generation for the generator network, (b) vector-by-matrix-multiplication with unprecedented energy-efficiency during the forward pass and backward propagation and (C) in-situ adversarial training using a hardware friendly Manhattan’s rule. |
Siddharth Satyam; Honey Nikam; Shubham Sahay; | arxiv-cs.ET | 2021-11-29 |
| 423 | Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we proposed a new framework, Generative Adversarial Networks with Conditional Neural Movement Primitives (GAN-CNMP), that incorporates a novel adversarial loss on CNMP to increase sketch smoothness and consistency. |
Suzan Ece Ada; M. Yunus Seker; | arxiv-cs.GR | 2021-11-29 |
| 424 | Continuous Conditional Generative Adversarial Networks for Data-driven Solutions of Poroelasticity with Heterogeneous Material Properties Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, our previous approach of conditional generative adversarial networks (cGAN) developed for the solution of steady-state problems involving highly heterogeneous material properties is extended to time-dependent problems by adopting the concept of continuous cGAN (CcGAN). |
T. KADEETHUM et. al. | arxiv-cs.CE | 2021-11-29 |
| 425 | Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The mode collapse problem is introduced and various methods, including minibatch GAN, unrolled GAN, BourGAN, mixture GAN, D2GAN, and Wasserstein GAN, are introduced for resolving this problem. |
Benyamin Ghojogh; Ali Ghodsi; Fakhri Karray; Mark Crowley; | arxiv-cs.LG | 2021-11-25 |
| 426 | Clustering Effect of (Linearized) Adversarial Robust Models Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we interpret adversarial robustness from the perspective of linear components, and find that there exist some statistical properties for comprehensively robust models. |
Yang Bai; Xin Yan; Yong Jiang; Shu-Tao Xia; Yisen Wang; | arxiv-cs.LG | 2021-11-25 |
| 427 | Optimizing Latent Space Directions For GAN-based Local Image Editing Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We thus present a novel objective function to evaluate the locality of an image edit. |
Ehsan Pajouheshgar; Tong Zhang; Sabine Süsstrunk; | arxiv-cs.CV | 2021-11-24 |
| 428 | A Method for Evaluating The Capacity of Generative Adversarial Networks to Reproduce High-order Spatial Context Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we demonstrate several tests of the statistical accuracy of images output by two popular GAN architectures. |
Rucha Deshpande; Mark A. Anastasio; Frank J. Brooks; | arxiv-cs.CV | 2021-11-24 |
| 429 | LDDMM Meets GANs: Generative Adversarial Networks for Diffeomorphic Registration Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The purpose of this work is to contribute to the state of the art of deep-learning methods for diffeomorphic registration. |
Ubaldo Ramon; Monica Hernandez; Elvira Mayordomo; | arxiv-cs.CV | 2021-11-24 |
| 430 | Thundernna: A White Box Adversarial Attack Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In This project, we develop a first-order method to attack the neural network. |
Linfeng Ye; | arxiv-cs.LG | 2021-11-24 |
| 431 | Robustness Against Adversarial Attacks in Neural Networks Using Incremental Dissipativity Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work proposes an incremental dissipativity-based robustness certificate for neural networks in the form of a linear matrix inequality for each layer. |
Bernardo Aquino; Arash Rahnama; Peter Seiler; Lizhen Lin; Vijay Gupta; | arxiv-cs.LG | 2021-11-24 |
| 432 | Challenges of Adversarial Image Augmentations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show that random augmentations are still competitive compared to an optimal adversarial approach, as well as to simple curricula, and conjecture that the success of AdvAA is due to the stochasticity of the policy controller network, which introduces a mild form of curriculum. |
Arno Blaas; Xavier Suau; Jason Ramapuram; Nicholas Apostoloff; Luca Zappella; | arxiv-cs.LG | 2021-11-24 |
| 433 | Distribution Estimation to Automate Transformation Policies for Self-Supervision Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Building on this observation, we propose a framework based on generative adversarial network to automatically find the transformations which are not present in the input dataset and thus effective for the self-supervised learning. |
Seunghan Yang; Debasmit Das; Simyung Chang; Sungrack Yun; Fatih Porikli; | arxiv-cs.CV | 2021-11-23 |
| 434 | Generative Adversarial Networks for Astronomical Images Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this research, using a Lightweight GAN, a dataset of images obtained from the web, and the Galaxy Zoo Dataset, we have generated thousands of new images of celestial bodies, galaxies, and finally, by combining them, a wide view of the universe. |
Davide Coccomini; Nicola Messina; Claudio Gennaro; Fabrizio Falchi; | arxiv-cs.CV | 2021-11-22 |
| 435 | Adversarial Examples on Segmentation Models Can Be Easy to Transfer Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we intensively study this topic. |
Jindong Gu; Hengshuang Zhao; Volker Tresp; Philip Torr; | arxiv-cs.CV | 2021-11-22 |
| 436 | Denoised Internal Models: A Brain-Inspired Autoencoder Against Adversarial Attacks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by recent advances in brain science, we propose the Denoised Internal Models (DIM), a novel generative autoencoder-based model to tackle this challenge. |
KAIYUAN LIU et. al. | arxiv-cs.CV | 2021-11-21 |
| 437 | Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To promote diversity in sample generation without degrading the overall quality, we propose a simple yet effective method to diagnose and emphasize underrepresented samples during training of a GAN. |
Jinhee Lee; Haeri Kim; Youngkyu Hong; Hye Won Chung; | nips | 2021-11-20 |
| 438 | Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To measure the difference between two probability distributions, referred to as the source and target, respectively, we exploit both the chain rule and Bayes’ theorem to construct conditional transport (CT), which is constituted by both a forward component and a backward one. |
Huangjie Zheng; Mingyuan Zhou; | nips | 2021-11-20 |
| 439 | Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce two cures for ACGAN. |
Minguk Kang; Woohyeon Shim; Minsu Cho; Jaesik Park; | nips | 2021-11-20 |
| 440 | Fast Extra Gradient Methods for Smooth Structured Nonconvex-Nonconcave Minimax Problems Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Built upon EG+ and EAG, this paper proposes a two-time-scale EG with anchoring, named fast extragradient (FEG), that has a fast $\mathcal{O}(1/k^2)$ rate on the squared gradient norm for smooth structured nonconvex-nonconcave problems; the corresponding saddle-gradient operator satisfies the negative comonotonicity condition. |
Sucheol Lee; Donghwan Kim; | nips | 2021-11-20 |
| 441 | Unsupervised Noise Adaptive Speech Enhancement By Discriminator-Constrained Optimal Transport Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel discriminator-constrained optimal transport network (DOTN) that performs unsupervised domain adaptation for speech enhancement (SE), which is an essential regression task in speech processing. |
Hsin-Yi Lin; Huan-Hsin Tseng; Xugang Lu; Yu Tsao; | nips | 2021-11-20 |
| 442 | Alias-Free Generative Adversarial Networks IF:4 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Interpreting all signals in the network as continuous, we derive generally applicable, small architectural changes that guarantee that unwanted information cannot leak into the hierarchical synthesis process. |
TERO KARRAS et. al. | nips | 2021-11-20 |
| 443 | Time-series Generation By Contrastive Imitation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy, where the reinforcement signal is provided by a global (but stepwise-decomposable) energy model trained by contrastive estimation. |
Daniel Jarrett; Ioana Bica; Mihaela van der Schaar; | nips | 2021-11-20 |
| 444 | CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a continual learning approach for generative adversarial networks (GANs), by designing and leveraging parameter-efficient feature map transformations. |
Sakshi Varshney; Vinay Kumar Verma; P. K. Srijith; Lawrence Carin; Piyush Rai; | nips | 2021-11-20 |
| 445 | AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an Attribute Guided Attention Generative Adversarial Network which employs novel attribute guided attention (AGA) modules to identify and focus the generation process on various facial features in the image. |
Abhishek Srivastava; Sukalpa Chanda; Umapada Pal; | arxiv-cs.CV | 2021-11-20 |
| 446 | Projected GANs Converge Faster IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by the finding that the discriminator cannot fully exploit features from deeper layers of the pretrained model, we propose a more effective strategy that mixes features across channels and resolutions. |
Axel Sauer; Kashyap Chitta; Jens M�ller; Andreas Geiger; | nips | 2021-11-20 |
| 447 | Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address this gap via a comprehensive investigation on the impact of network width and depth on the robustness of adversarially trained DNNs. |
HANXUN HUANG et. al. | nips | 2021-11-20 |
| 448 | Do Wider Neural Networks Really Help Adversarial Robustness? IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we carefully examine the relationship between network width and model robustness. |
Boxi Wu; Jinghui Chen; Deng Cai; Xiaofei He; Quanquan Gu; | nips | 2021-11-20 |
| 449 | Adversarial Examples Make Strong Poisons IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show that adversarial examples, originally intended for attacking pre-trained models, are even more effective for data poisoning than recent methods designed specifically for poisoning. |
LIAM FOWL et. al. | nips | 2021-11-20 |
| 450 | Can We Have It All? On The Trade-off Between Spatial and Adversarial Robustness of Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we prove a quantitative trade-off between spatial and adversarial robustness in a simple statistical setting. |
Sandesh Kamath; Amit Deshpande; Subrahmanyam Kambhampati Venkata; Vineeth N Balasubramanian; | nips | 2021-11-20 |
| 451 | Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a neural ODE with Lyapunov-stable equilibrium points for defending against adversarial attacks (SODEF). |
Qiyu Kang; Yang Song; Qinxu Ding; Wee Peng Tay; | nips | 2021-11-20 |
| 452 | Towards Efficient and Effective Adversarial Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we bridge this performance gap by introducing a novel Nuclear-Norm regularizer on network predictions to enforce function smoothing in the vicinity of data samples. |
Gaurang Sriramanan; Sravanti Addepalli; Arya Baburaj; Venkatesh Babu R; | nips | 2021-11-20 |
| 453 | Neural Architecture Dilation for Adversarial Robustness Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: From the neural architecture perspective, this paper aims to improve the adversarial robustness of the backbone CNNs that have a satisfactory accuracy. |
Yanxi Li; Zhaohui Yang; Yunhe Wang; Chang Xu; | nips | 2021-11-20 |
| 454 | A Little Robustness Goes A Long Way: Leveraging Robust Features for Targeted Transfer Attacks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we show that training the source classifier to be slightly robust-that is, robust to small-magnitude adversarial examples—substantially improves the transferability of class-targeted and representation-targeted adversarial attacks, even between architectures as different as convolutional neural networks and transformers. |
Jacob Springer; Melanie Mitchell; Garrett Kenyon; | nips | 2021-11-20 |
| 455 | Neural Population Geometry Reveals The Role of Stochasticity in Robust Perception Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, using recently developed geometrical techniques from computational neuroscience, we investigate how adversarial perturbations influence the internal representations of standard, adversarially trained, and biologically-inspired stochastic networks. |
JOEL DAPELLO et. al. | nips | 2021-11-20 |
| 456 | Clustering Effect of Adversarial Robust Models Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we interpret adversarial robustness from the perspective of linear components, and find that there exist some statistical properties for comprehensively robust models. |
Yang Bai; Xin Yan; Yong Jiang; Shu-Tao Xia; Yisen Wang; | nips | 2021-11-20 |
| 457 | Encoding Robustness to Image Style Via Adversarial Feature Perturbations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our proposed method, Adversarial Batch Normalization (AdvBN), is a single network layer that generates worst-case feature perturbations during training. By fine-tuning neural networks on adversarial feature distributions, we observe improved robustness of networks to various unseen distributional shifts, including style variations and image corruptions. |
Manli Shu; Zuxuan Wu; Micah Goldblum; Tom Goldstein; | nips | 2021-11-20 |
| 458 | Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate the HSIC (Hilbert-Schmidt independence criterion) bottleneck as a regularizer for learning an adversarially robust deep neural network classifier. |
Zifeng Wang; Tong Jian; Aria Masoomi; Stratis Ioannidis; Jennifer Dy; | nips | 2021-11-20 |
| 459 | Adversarial Feature Desensitization Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel approach to adversarial robustness, which builds upon the insights from the domain adaptation field. |
POUYA BASHIVAN et. al. | nips | 2021-11-20 |
| 460 | Adversarial Attacks on Graph Classifiers Via Bayesian Optimisation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel Bayesian optimisation-based attack method for graph classification models. |
XINGCHEN WAN et. al. | nips | 2021-11-20 |
| 461 | A Single Gradient Step Finds Adversarial Examples on Random Two-layers Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In fact we prove that a single step of gradient descent suffices. We also show this result for any subexponential width random neural network with smooth activation function. |
Sebastien Bubeck; Yeshwanth Cherapanamjeri; Gauthier Gidel; Remi Tachet des Combes; | nips | 2021-11-20 |
| 462 | Learning Transferable Adversarial Perturbations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we, therefore, investigate the transferability of generated perturbations when the conditions at inference time differ from the training ones in terms of the target architecture, target data, and target task. |
Krishna kanth Nakka; Mathieu Salzmann; | nips | 2021-11-20 |
| 463 | Adversarial Deep Learning for Online Resource Allocation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To ensure better convergence of the algorithm network (to the desired online algorithm), we propose a novel per-round update method to handle sequential decision making to break complex dependency among different rounds so that update can be done for every possible action, instead of only sampled actions. |
Bingqian Du; Zhiyi Huang; Chuan Wu; | arxiv-cs.LG | 2021-11-19 |
| 464 | Meta Adversarial Perturbations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show the existence of a meta adversarial perturbation (MAP), a better initialization that causes natural images to be misclassified with high probability after being updated through only a one-step gradient ascent update, and propose an algorithm for computing such perturbations. |
Chia-Hung Yuan; Pin-Yu Chen; Chia-Mu Yu; | arxiv-cs.LG | 2021-11-19 |
| 465 | TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Through extensive experiments on the large-scale visual classification task, ImageNet with evaluations across its entire validation set of 50,000 images, we demonstrate the realistic threat from TnTs and the robustness of the attack. |
Bao Gia Doan; Minhui Xue; Shiqing Ma; Ehsan Abbasnejad; Damith C. Ranasinghe; | arxiv-cs.CV | 2021-11-18 |
| 466 | One-Shot Generative Domain Adaptation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work aims at transferring a Generative Adversarial Network (GAN) pre-trained on one image domain to a new domain referring to as few as just one target image. |
CEYUAN YANG et. al. | arxiv-cs.CV | 2021-11-18 |
| 467 | SeCGAN: Parallel Conditional Generative Adversarial Networks for Face Editing Via Semantic Consistency Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose SeCGAN, a novel label-guided cGAN for editing face images utilising semantic information without the need to specify target semantic masks. |
Jiaze Sun; Binod Bhattarai; Zhixiang Chen; Tae-Kyun Kim; | arxiv-cs.CV | 2021-11-17 |
| 468 | Generating Unrestricted 3D Adversarial Point Clouds Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an Adversarial Graph-Convolutional Generative Adversarial Network (AdvGCGAN) to generate visually realistic adversarial 3D point clouds from scratch. |
Xuelong Dai; Yanjie Li; Hua Dai; Bin Xiao; | arxiv-cs.CV | 2021-11-17 |
| 469 | Self-Attending Task Generative Adversarial Network for Realistic Satellite Image Creation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a self-attending task generative adversarial network (SATGAN) and apply it to the problem of augmenting synthetic high contrast scientific imagery of resident space objects with realistic noise patterns and sensor characteristics learned from collected data. |
Nathan Toner; Justin Fletcher; | arxiv-cs.LG | 2021-11-17 |
| 470 | Attacking Deep Learning AI Hardware with Universal Adversarial Perturbation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we demonstrate an attack strategy that when activated by rogue means (e.g., malware, trojan) can bypass these existing countermeasures by augmenting the adversarial noise at the AI hardware accelerator stage. |
Mehdi Sadi; B. M. S. Bahar Talukder; Kaniz Mishty; Md Tauhidur Rahman; | arxiv-cs.CR | 2021-11-17 |
| 471 | Synthesis-Guided Feature Learning for Cross-Spectral Periocular Recognition Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel approach to cross-spectral periocular verification that primarily focuses on learning a mapping from visible and NIR periocular images to a shared latent representational subspace, and supports this effort by simultaneously learning intra-spectral image reconstruction. |
Domenick Poster; Nasser Nasrabadi; | arxiv-cs.CV | 2021-11-16 |
| 472 | Detecting AutoAttack Perturbations in The Frequency Domain Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the spatial and frequency domain properties of AutoAttack and propose an alternative defense. |
Peter Lorenz; Paula Harder; Dominik Strassel; Margret Keuper; Janis Keuper; | arxiv-cs.CV | 2021-11-16 |
| 473 | Improving The Robustness and Accuracy of Biomedical Language Models Through Adversarial Training Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We implemented various adversarial attack methods to test the NLP systems in different attack scenarios. |
Milad Moradi; Matthias Samwald; | arxiv-cs.CL | 2021-11-16 |
| 474 | Robustness of Bayesian Neural Networks to White-Box Adversarial Attacks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, we investigate the robustness of BNNs to white-box attacks using multiple Bayesian neural architectures. |
Adaku Uchendu; Daniel Campoy; Christopher Menart; Alexandra Hildenbrandt; | arxiv-cs.LG | 2021-11-16 |
| 475 | Lifelong Vehicle Trajectory Prediction Framework Based on Generative Replay Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To maintain consistent performance as a vehicle driving around different cities, it is crucial to adapt to changing traffic circumstances and achieve lifelong trajectory prediction model. |
Peng Bao; Zonghai Chen; Jikai Wang; Deyun Dai; Hao Zhao; | arxiv-cs.RO | 2021-11-14 |
| 476 | Generating Band-Limited Adversarial Surfaces Using Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this technical report we suggest a neural network that generates the attacks. |
Roee Ben Shlomo; Yevgeniy Men; Ido Imanuel; | arxiv-cs.CV | 2021-11-14 |
| 477 | Introducing Variational Autoencoders to High School Students Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes the lesson design and shares insights from the pilot studies with 22 students. |
Zhuoyue Lyu; Safinah Ali; Cynthia Breazeal; | arxiv-cs.CY | 2021-11-12 |
| 478 | Training Generative Adversarial Networks with Adaptive Composite Gradient Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposed the adaptive Composite Gradients (ACG) method, linearly convergent in bilinear games under suitable settings. |
Huiqing Qi; Fang Li; Shengli Tan; Xiangyun Zhang; | arxiv-cs.LG | 2021-11-09 |
| 479 | Sparse Adversarial Video Attacks with Spatial Transformations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an adversarial attack strategy on videos, called DeepSAVA. |
Ronghui Mu; Wenjie Ruan; Leandro Soriano Marcolino; Qiang Ni; | arxiv-cs.CV | 2021-11-09 |
| 480 | Teamwork Makes Von Neumann Work: Min-Max Optimization in Two-Team Zero-Sum Games Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we present a family of team games whose induced utility is \emph{non} multi-linear with \emph{non} attractive \emph{per-se} mixed Nash Equilibria, as strict saddle points of the underlying optimization landscape. |
Fivos Kalogiannis; Ioannis Panageas; Emmanouil-Vasileios Vlatakis-Gkaragkounis; | arxiv-cs.GT | 2021-11-07 |
| 481 | Generative Dynamic Patch Attack Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that GDPA is a generic attack framework that can produce dynamic/static and visible/invisible patches with a few configuration changes. |
Xiang Li; Shihao Ji; | arxiv-cs.CV | 2021-11-07 |
| 482 | ActFloor-GAN: Activity-Guided Adversarial Networks for Human-Centric Floorplan Design Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel two-stage approach for automated floorplan design in residential buildings with a given exterior wall boundary. |
SHIDONG WANG et. al. | arxiv-cs.GR | 2021-11-05 |
| 483 | On The Transferability of Adversarial Attacks Against Neural Text Classifier Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models. |
Liping Yuan; Xiaoqing Zheng; Yi Zhou; Cho-Jui Hsieh; Kai-Wei Chang; | emnlp | 2021-11-05 |
| 484 | Searching for An Effective Defender: Benchmarking Defense Against Adversarial Word Substitution Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we seek to fill the gap of systematic studies through comprehensive researches on understanding the behavior of neural text classifiers trained by various defense methods under representative adversarial attacks. |
ZONGYI LI et. al. | emnlp | 2021-11-05 |
| 485 | Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework based on generative adversarial network (GAN) combined with numerical model to predict sea subsurface temperature. |
YUXIN MENG et. al. | arxiv-cs.LG | 2021-11-04 |
| 486 | Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a deep learning model for data-driven simulations of random dynamical systems without a distributional assumption. |
Kyongmin Yeo; Zan Li; Wesley M. Gifford; | arxiv-cs.LG | 2021-11-04 |
| 487 | GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Under such a class of discriminator (or critic) functions, we present Gradient Normalization (GraN), a novel input-dependent normalization method, which guarantees a piecewise K-Lipschitz constraint in the input space. |
Vineeth S. Bhaskara; Tristan Aumentado-Armstrong; Allan Jepson; Alex Levinshtein; | arxiv-cs.LG | 2021-11-04 |
| 488 | Improving Model Compatibility of Generative Adversarial Networks By Boundary Calibration Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To improve GAN in terms of model compatibility, we propose Boundary-Calibration GANs (BCGANs), which leverage the boundary information from a set of pre-trained classifiers using the original data. |
Si-An Chen; Chun-Liang Li; Hsuan-Tien Lin; | arxiv-cs.LG | 2021-11-03 |
| 489 | Discriminator Synthesis: On Reusing The Other Half of Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. |
Diego Porres; | arxiv-cs.CV | 2021-11-03 |
| 490 | Convolutional Generative Adversarial Imputation Networks for Spatio-temporal Missing Data in Storm Surge Simulations Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, the Generative Adversarial Imputation Nets (GAIN) performance is improved by applying convolutional neural networks instead of fully connected layers to better capture the correlation of data and promote learning from the adjacent surge points. |
Ehsan Adeli; Jize Zhang; Alexandros A. Taflanidis; | arxiv-cs.LG | 2021-11-02 |
| 491 | Spiking Generative Adversarial Networks With A Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In contrast, in order to fully leverage the time-encoding capacity of spikes, this work proposes to train SNNs so as to match distributions of spiking signals rather than individual spiking signals. |
Bleema Rosenfeld; Osvaldo Simeone; Bipin Rajendran; | arxiv-cs.LG | 2021-11-02 |
| 492 | Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose DP-Sinkhorn, a novel optimal transport-based generative method for learning data distributions from private data with differential privacy. |
Tianshi Cao; Alex Bie; Arash Vahdat; Sanja Fidler; Karsten Kreis; | arxiv-cs.LG | 2021-11-01 |
| 493 | Projected GANs Converge Faster IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by the finding that the discriminator cannot fully exploit features from deeper layers of the pretrained model, we propose a more effective strategy that mixes features across channels and resolutions. |
Axel Sauer; Kashyap Chitta; Jens Müller; Andreas Geiger; | arxiv-cs.CV | 2021-11-01 |
| 494 | FREGAN : An Application of Generative Adversarial Networks in Enhancing The Frame Rate of Videos Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigated the GAN model and proposed FREGAN for the enhancement of frame rate in videos. |
Rishik Mishra; Neeraj Gupta; Nitya Shukla; | arxiv-cs.CV | 2021-11-01 |
| 495 | Attention-Guided Generative Adversarial Network for Whisper to Normal Speech Conversion Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a novel attention-guided generative adversarial network model incorporating an autoencoder, a Siamese neural network, and an identity mapping loss function for whisper to normal speech conversion (AGAN-W2SC) is proposed. |
Teng Gao; Jian Zhou; Huabin Wang; Liang Tao; Hon Keung Kwan; | arxiv-cs.SD | 2021-11-01 |
| 496 | ECG Synthesis with Neural ODE and GAN Models Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we used Neural ODE based model to generate synthetic sine waves and synthetic ECG. |
Mansura Habiba; Eoin Brophy; Barak A. Pearlmutter; Tomas Ward; | arxiv-cs.LG | 2021-10-30 |
| 497 | Improving The Quality of Generative Models Through Smirnov Transformation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel activation function to be used as output of the generator agent. |
Ángel González-Prieto; Alberto Mozo; Sandra Gómez-Canaval; Edgar Talavera; | arxiv-cs.LG | 2021-10-29 |
| 498 | Visual Explanations for Convolutional Neural Networks Via Latent Traversal of Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Using COVID-19 chest X-rays, we present a method for interpreting what a CNN has learned by utilizing Generative Adversarial Networks (GANs). |
Amil Dravid; Aggelos K. Katsaggelos; | arxiv-cs.CV | 2021-10-29 |
| 499 | AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on this observation, we propose the adversarial extreme value analysis(AEVA) to detect backdoors in black-box neural networks. |
Junfeng Guo; Ang Li; Cong Liu; | arxiv-cs.LG | 2021-10-28 |
| 500 | NIDA-CLIFGAN: Natural Infrastructure Damage Assessment Through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Accordingly, the paper demonstrates a systematic effort to achieve efficient building damage classification. |
JIE WEI et. al. | arxiv-cs.LG | 2021-10-27 |
| 501 | Fuzzy Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces techniques that show improvement in the GANs’ regression capability through mean absolute error (MAE) and mean squared error (MSE). |
Ryan Nguyen; Shubhendu Kumar Singh; Rahul Rai; | arxiv-cs.LG | 2021-10-27 |
| 502 | CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate GNNs from the lens of weight and feature loss landscapes, i.e., the loss changes with respect to model weights and node features, respectively. To tackle this problem, we construct the co-adversarial perturbation (CAP) optimization problem in terms of weights and features, and design the alternating adversarial perturbation algorithm to flatten the weight and feature loss landscapes alternately. |
HAOTIAN XUE et. al. | arxiv-cs.LG | 2021-10-27 |
| 503 | Frequency Centric Defense Mechanisms Against Adversarial Examples Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Adversarial example (AE) aims at fooling a Convolution Neural Network by introducing small perturbations in the input image.The proposed work uses the magnitude and phase of the Fourier Spectrum and the entropy of the image to defend against AE. |
Sanket B. Shah; Param Raval; Harin Khakhi; Mehul S. Raval; | arxiv-cs.CV | 2021-10-26 |
| 504 | Can’t Fool Me: Adversarially Robust Transformer for Video Understanding Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to bridge this gap. |
Divya Choudhary; Palash Goyal; Saurabh Sahu; | arxiv-cs.CV | 2021-10-26 |
| 505 | Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found Within Randomly Initialized Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: Interestingly, we discover for the first time that there exist subnetworks with inborn robustness, matching or surpassing the robust accuracy of the adversarially trained networks with comparable model sizes, within randomly initialized networks without any model training, indicating that adversarial training on model weights is not indispensable towards adversarial robustness. |
YONGGAN FU et. al. | arxiv-cs.LG | 2021-10-26 |
| 506 | Towards Realistic Market Simulations: A Generative Adversarial Networks Approach Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In contrast, multi-agent simulation presents a natural bottom-up approach to emulating agent interaction in financial markets. |
ANDREA COLETTA et. al. | arxiv-cs.AI | 2021-10-25 |
| 507 | Negotiating Networks in Oligopoly Markets for Price-Sensitive Products Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel framework to learn functions that estimate decisions of sellers and buyers simultaneously in an oligopoly market for a price-sensitive product. |
Naman Shukla; Kartik Yellepeddi; | arxiv-cs.LG | 2021-10-25 |
| 508 | EarthGAN: Can We Visualize The Earth’s Mantle Convection Using A Surrogate Model? Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this research, we seek to build a surrogate model, using a generative adversarial network, to allow for the visualization of the Earth’s Mantle Convection data set on readily accessible hardware. |
Tim von Hahn; Chris K. Mechefske; | arxiv-cs.LG | 2021-10-25 |
| 509 | Generating Watermarked Adversarial Texts Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to tackle with this problem, we present a general framework for generating watermarked adversarial text examples. |
Mingjie Li; Hanzhou Wu; Xinpeng Zhang; | arxiv-cs.CR | 2021-10-25 |
| 510 | Quality Map Fusion for Adversarial Learning Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we improve image quality adversarially by introducing a novel quality map fusion technique that harnesses image features similar to the HVS and the perceptual properties of a deep convolutional neural network (DCNN). |
Uche Osahor; Nasser M. Nasrabadi; | arxiv-cs.CV | 2021-10-23 |
| 511 | Face Sketch to Photo Translation Using Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a method for converting an input facial sketch to a colorful photo without the need for any paired dataset. |
Nastaran Moradzadeh Farid; Maryam Saeedi Fard; Ahmad Nickabadi; | arxiv-cs.CV | 2021-10-23 |
| 512 | A Layer-wise Adversarial-aware Quantization Optimization for Improving Robustness Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we find that adversarially-trained neural networks are more vulnerable to quantization loss than plain models. |
Chang Song; Riya Ranjan; Hai Li; | arxiv-cs.LG | 2021-10-23 |
| 513 | Generative Adversarial Networks for Non-Raytraced Global Illumination on Older GPU Hardware Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We give an overview of the different rendering methods and we demonstrate that the use of a Generative Adversarial Networks (GAN) for Global Illumination (GI) gives a superior quality rendered image to that of a rasterisations image. |
Jared Harris-Dewey; Richard Klein; | arxiv-cs.CV | 2021-10-22 |
| 514 | Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a systematic literature review of the applications of GANs in anomaly detection, covering 128 papers on the subject. |
Mikael Sabuhi; Ming Zhou; Cor-Paul Bezemer; Petr Musilek; | arxiv-cs.LG | 2021-10-22 |
| 515 | How and When Adversarial Robustness Transfers in Knowledge Distillation? Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that standard KD training fails to preserve adversarial robustness, and we propose KD with input gradient alignment (KDIGA) for remedy. |
Rulin Shao; Jinfeng Yi; Pin-Yu Chen; Cho-Jui Hsieh; | arxiv-cs.LG | 2021-10-22 |
| 516 | Adversarial Robustness for Latent Models: Revisiting The Robust-standard Accuracies Tradeoff Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This phenomenon has intrigued the research community to investigate the potential tradeoff between standard accuracy (a.k.a generalization) and robust accuracy (a.k.a robust generalization) as two performance measures. In this paper, we revisit this tradeoff for latent models and argue that this tradeoff is mitigated when the data enjoys a low-dimensional structure. |
Adel Javanmard; Mohammad Mehrabi; | arxiv-cs.LG | 2021-10-22 |
| 517 | Super-resolution of Multiphase Materials By Combining Complementary 2D and 3D Image Data Using Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a method for combining information from pairs of distinct but complementary imaging techniques in order to accurately reconstruct the desired multi-phase, high resolution, representative, 3D images. |
Amir Dahari; Steve Kench; Isaac Squires; Samuel J. Cooper; | arxiv-cs.CV | 2021-10-21 |
| 518 | On Some Theoretical Limitations of Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We provide a new result based on Extreme Value Theory showing that GANs can’t generate heavy tailed distributions. |
Benoît Oriol; Alexandre Miot; | arxiv-cs.LG | 2021-10-21 |
| 519 | An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we perform an empirical study on the impact of several loss functions on the performance of standard GAN models, Deep Convolutional Generative Adversarial Networks (DCGANs). |
Cuong V. Nguyen; Tien-Dung Cao; Tram Truong-Huu; Khanh N. Pham; Binh T. Nguyen; | arxiv-cs.CV | 2021-10-21 |
| 520 | Generative Adversarial Graph Convolutional Networks for Human Action Synthesis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Kinetic-GAN, a novel architecture that leverages the benefits of Generative Adversarial Networks and Graph Convolutional Networks to synthesise the kinetics of the human body. |
BRUNO DEGARDIN et. al. | arxiv-cs.CV | 2021-10-21 |
| 521 | PROVES: Establishing Image Provenance Using Semantic Signatures Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel architecture for preserving the provenance of semantic information in images to make them less susceptible to deep fake attacks. |
MINGYANG XIE et. al. | arxiv-cs.CV | 2021-10-21 |
| 522 | Deep Generative Models in Engineering Design: A Review Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a review and analysis of Deep Generative Machine Learning models in engineering design. |
Lyle Regenwetter; Amin Heyrani Nobari; Faez Ahmed; | arxiv-cs.LG | 2021-10-20 |
| 523 | Fine-Grained Control of Artistic Styles in Image Generation Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to achieve this by embedding original artwork examples into a continuous style space. |
XIN MIAO et. al. | arxiv-cs.CV | 2021-10-19 |
| 524 | Protecting Anonymous Speech: A Generative Adversarial Network Methodology for Removing Stylistic Indicators in Text Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a new approach to authorship anonymization by constructing a generative adversarial network that protects identity and optimizes for three different losses corresponding to anonymity, fluency, and content preservation. |
Rishi Balakrishnan; Stephen Sloan; Anil Aswani; | arxiv-cs.LG | 2021-10-18 |
| 525 | Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, in this paper, we investigate several adversarial models for domain adaptation (DA) and their effect on the acoustic scene classification task. |
Stanisław Kacprzak; Konrad Kowalczyk; | arxiv-cs.SD | 2021-10-18 |
| 526 | Continuation of Famous Art with AI: A Conditional Adversarial Network Inpainting Approach Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work explores the application of image inpainting to continue famous artworks and produce generative art with a Conditional GAN. |
Jordan J. Bird; | arxiv-cs.CV | 2021-10-18 |
| 527 | Neural Synthesis of Footsteps Sound Effects with Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a first attempt at adopting neural synthesis for this task. |
Marco Comunità; Huy Phan; Joshua D. Reiss; | arxiv-cs.SD | 2021-10-18 |
| 528 | Towards Better Long-range Time Series Forecasting Using Generative Adversarial Networks Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we utilize a Conditional Wasserstein GAN (CWGAN) and augment it with an error penalty term, leading to a new generative model which aims to generate high-quality synthetic time series data, called CWGAN-TS. |
Shiyu Liu; Mehul Motani; | arxiv-cs.LG | 2021-10-17 |
| 529 | Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose two variations of GAIL for autonomous navigation of a vehicle in the realistic CARLA simulation environment for urban scenarios. |
Gustavo Claudio Karl Couto; Eric Aislan Antonelo; | arxiv-cs.RO | 2021-10-16 |
| 530 | Pose-guided Generative Adversarial Net for Novel View Action Synthesis Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we propose a novel framework named Pose-guided Action Separable Generative Adversarial Net (PAS-GAN), which utilizes pose to alleviate the difficulty of this task. |
Xianhang Li; Junhao Zhang; Kunchang Li; Shruti Vyas; Yogesh S Rawat; | arxiv-cs.CV | 2021-10-15 |
| 531 | Adversarial Attacks on ML Defense Models Competition Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: The participants were encouraged to develop stronger white-box attack algorithms to find the worst-case robustness of different defenses. |
YINPENG DONG et. al. | arxiv-cs.CV | 2021-10-15 |
| 532 | SpecSinGAN: Sound Effect Variation Synthesis Using Single-Image GANs Literature Review Related Patents Related Grants |