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Theoretically Principled Trade-off between Robustness and Accuracy

24 January 2019

Papers citing "Theoretically Principled Trade-off between Robustness and Accuracy"

50 / 837 papers shown

Title
Adversarial Feature Desensitization P. Bashivan Reza Bayat Adam Ibrahim Kartik Ahuja Mojtaba Faramarzi Touraj Laleh Blake A. Richards Irina Rish AAML 50 21 0 08 Jun 2020
Consistency Regularization for Certified Robustness of Smoothed Classifiers Jongheon Jeong Jinwoo Shin AAML 86 88 0 07 Jun 2020
Principled learning method for Wasserstein distributionally robust optimization with local perturbations Yongchan Kwon Wonyoung Hedge Kim Joong-Ho Won M. Paik 96 12 0 05 Jun 2020
Second-Order Provable Defenses against Adversarial Attacks Sahil Singla Soheil Feizi AAML 74 60 0 01 Jun 2020
Calibrated Surrogate Losses for Adversarially Robust Classification Han Bao Clayton Scott Masashi Sugiyama 78 46 0 28 May 2020
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models Mitch Hill Jonathan Mitchell Song-Chun Zhu AAML 92 71 0 27 May 2020
Feature Purification: How Adversarial Training Performs Robust Deep Learning Zeyuan Allen-Zhu Yuanzhi Li MLT AAML 122 151 0 20 May 2020
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks Linhai Ma Liang Liang AAML 137 19 0 19 May 2020
Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition Tianhang Zheng Sheng Liu Changyou Chen Junsong Yuan Baochun Li K. Ren AAML 83 17 0 14 May 2020
DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses Yaxin Li Wei Jin Han Xu Jiliang Tang AAML 90 133 0 13 May 2020
Class-Aware Domain Adaptation for Improving Adversarial Robustness Xianxu Hou Jingxin Liu Bolei Xu Xiaolong Wang Bozhi Liu Guoping Qiu OOD AAML 125 9 0 10 May 2020
Adversarial Training against Location-Optimized Adversarial Patches Sukrut Rao David Stutz Bernt Schiele AAML 84 92 0 05 May 2020
Robust Encodings: A Framework for Combating Adversarial Typos Erik Jones Robin Jia Aditi Raghunathan Percy Liang AAML 321 103 0 04 May 2020
Towards Feature Space Adversarial Attack Qiuling Xu Guanhong Tao Shuyang Cheng Xinming Zhang GAN AAML 66 25 0 26 Apr 2020
Improved Adversarial Training via Learned Optimizer Yuanhao Xiong Cho-Jui Hsieh AAML 77 31 0 25 Apr 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Helen Zhou AAML 59 8 0 23 Apr 2020
Single-step Adversarial training with Dropout Scheduling S. VivekB. R. Venkatesh Babu OOD AAML 65 73 0 18 Apr 2020
Towards Transferable Adversarial Attack against Deep Face Recognition Yaoyao Zhong Weihong Deng AAML 105 162 0 13 Apr 2020
Approximate Manifold Defense Against Multiple Adversarial Perturbations Jay Nandy Wynne Hsu Mong Li Lee AAML 65 12 0 05 Apr 2020
Towards Deep Learning Models Resistant to Large Perturbations Amirreza Shaeiri Rozhin Nobahari M. Rohban OOD AAML 81 12 0 30 Mar 2020
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning Tianlong Chen Sijia Liu Shiyu Chang Yu Cheng Lisa Amini Zhangyang Wang AAML 71 250 0 28 Mar 2020
DP-Net: Dynamic Programming Guided Deep Neural Network Compression Dingcheng Yang Wenjian Yu Ao Zhou Haoyuan Mu G. Yao Xiaoyi Wang 35 6 0 21 Mar 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 191 102 0 20 Mar 2020
One Neuron to Fool Them All Anshuman Suri David Evans AAML 31 4 0 20 Mar 2020
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations Huan Zhang Hongge Chen Chaowei Xiao Yue Liu Mingyan D. Liu Duane S. Boning Cho-Jui Hsieh AAML 176 275 0 19 Mar 2020
SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing Chawin Sitawarin S. Chakraborty David Wagner AAML 74 40 0 18 Mar 2020
Toward Adversarial Robustness via Semi-supervised Robust Training Yiming Li Baoyuan Wu Yan Feng Yanbo Fan Yong Jiang Zhifeng Li Shutao Xia AAML 121 13 0 16 Mar 2020
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks Y. Tashiro Yang Song Stefano Ermon AAML 81 13 0 15 Mar 2020
On the benefits of defining vicinal distributions in latent space Puneet Mangla Vedant Singh Shreyas Jayant Havaldar V. Balasubramanian AAML 21 3 0 14 Mar 2020
Manifold Regularization for Locally Stable Deep Neural Networks Charles Jin Martin Rinard AAML 91 15 0 09 Mar 2020
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization Saehyung Lee Hyungyu Lee Sungroh Yoon AAML 252 119 0 05 Mar 2020
A Closer Look at Accuracy vs. Robustness Yao-Yuan Yang Cyrus Rashtchian Hongyang R. Zhang Ruslan Salakhutdinov Kamalika Chaudhuri OOD 145 26 0 05 Mar 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 281 1,864 0 03 Mar 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 129 67 0 02 Mar 2020
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets Thu Dinh Bao Wang Andrea L. Bertozzi Stanley J. Osher AAML 34 17 0 02 Mar 2020
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models Xiao Zhang Jinghui Chen Quanquan Gu David Evans 76 17 0 01 Mar 2020
On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks Yue Zhao Yuwei Wu Caihua Chen A. Lim 3DPC 97 72 0 27 Feb 2020
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization Sicheng Zhu Xiao Zhang David Evans SSL OOD 93 27 0 26 Feb 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 159 811 0 26 Feb 2020
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks Sandesh Kamath Amit Deshpande Subrahmanyam Kambhampati Venkata V. Balasubramanian 88 12 0 26 Feb 2020
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger Jingfeng Zhang Xilie Xu Bo Han Gang Niu Li-zhen Cui Masashi Sugiyama Mohan S. Kankanhalli AAML 62 406 0 26 Feb 2020
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization Yifei Min Lin Chen Amin Karbasi AAML 103 69 0 25 Feb 2020
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Aditi Raghunathan Sang Michael Xie Fanny Yang John C. Duchi Percy Liang AAML 104 229 0 25 Feb 2020
HYDRA: Pruning Adversarially Robust Neural Networks Vikash Sehwag Shiqi Wang Prateek Mittal Suman Jana AAML 69 25 0 24 Feb 2020
Precise Tradeoffs in Adversarial Training for Linear Regression Adel Javanmard Mahdi Soltanolkotabi Hamed Hassani AAML 83 109 0 24 Feb 2020
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference Ting-Kuei Hu Tianlong Chen Haotao Wang Zhangyang Wang OOD AAML 3DH 95 84 0 24 Feb 2020
Robustness from Simple Classifiers Sharon Qian Dimitris Kalimeris Gal Kaplun Yaron Singer AAML 18 1 0 21 Feb 2020
MaxUp: A Simple Way to Improve Generalization of Neural Network Training Chengyue Gong Zhaolin Ren Mao Ye Qiang Liu AAML 77 56 0 20 Feb 2020
Boosting Adversarial Training with Hypersphere Embedding Tianyu Pang Xiao Yang Yinpeng Dong Kun Xu Jun Zhu Hang Su AAML 89 156 0 20 Feb 2020
Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness Huijie Feng Chunpeng Wu Guoyang Chen Weifeng Zhang Y. Ning AAML 71 11 0 17 Feb 2020