Adversarially Robust Generalization Requires More Data

30 April 2018

Papers citing "Adversarially Robust Generalization Requires More Data"

48 / 198 papers shown

Title
CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting Pranav Rajpurkar Anirudh Joshi Anuj Pareek Phil Chen Amirhossein Kiani Jeremy Irvin A. Ng M. Lungren LM&MA 27 49 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 39 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 51 224 0 25 Feb 2020
Gödel's Sentence Is An Adversarial Example But Unsolvable Xiaodong Qi Lansheng Han AAML 30 0 0 25 Feb 2020
MaxUp: A Simple Way to Improve Generalization of Neural Network Training Chengyue Gong Tongzheng Ren Mao Ye Qiang Liu AAML 29 56 0 20 Feb 2020
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models Lin Chen Yifei Min Mingrui Zhang Amin Karbasi OOD 38 64 0 11 Feb 2020
Understanding the Decision Boundary of Deep Neural Networks: An Empirical Study David Mickisch F. Assion Florens Greßner W. Günther M. Motta AAML 19 34 0 05 Feb 2020
Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study Jinlan Fu Pengfei Liu Qi Zhang Xuanjing Huang AI4CE 33 73 0 12 Jan 2020
Efficient Adversarial Training with Transferable Adversarial Examples Haizhong Zheng Ziqi Zhang Juncheng Gu Honglak Lee A. Prakash AAML 24 108 0 27 Dec 2019
Universal adversarial examples in speech command classification Jon Vadillo Roberto Santana AAML 34 29 0 22 Nov 2019
Where is the Bottleneck of Adversarial Learning with Unlabeled Data? Jingfeng Zhang Bo Han Gang Niu Tongliang Liu Masashi Sugiyama 30 6 0 20 Nov 2019
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey Olakunle Ibitoye Rana Abou-Khamis Mohamed el Shehaby Ashraf Matrawy M. O. Shafiq AAML 39 68 0 06 Nov 2019
Privacy Enhanced Multimodal Neural Representations for Emotion Recognition Mimansa Jaiswal E. Provost 42 73 0 29 Oct 2019
Understanding and Quantifying Adversarial Examples Existence in Linear Classification Xupeng Shi A. Ding AAML 19 3 0 27 Oct 2019
Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin Colin Wei Tengyu Ma AAML OOD 38 85 0 09 Oct 2019
A Closer Look at Data Bias in Neural Extractive Summarization Models Ming Zhong Danqing Wang Pengfei Liu Xipeng Qiu Xuanjing Huang 48 42 0 30 Sep 2019
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training Haichao Zhang Jianyu Wang AAML 23 230 0 24 Jul 2019
Adversarial Training Can Hurt Generalization Aditi Raghunathan Sang Michael Xie Fanny Yang John C. Duchi Percy Liang 21 240 0 14 Jun 2019
Lower Bounds for Adversarially Robust PAC Learning Dimitrios I. Diochnos Saeed Mahloujifar Mohammad Mahmoody AAML 27 26 0 13 Jun 2019
ML-LOO: Detecting Adversarial Examples with Feature Attribution Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Michael I. Jordan AAML 22 101 0 08 Jun 2019
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation Yuzhe Yang Guo Zhang Dina Katabi Zhi Xu AAML 15 168 0 28 May 2019
Robust Classification using Robust Feature Augmentation Kevin Eykholt Swati Gupta Atul Prakash Amir Rahmati Pratik Vaishnavi Haizhong Zheng AAML 19 2 0 26 May 2019
Privacy Risks of Securing Machine Learning Models against Adversarial Examples Liwei Song Reza Shokri Prateek Mittal SILM MIACV AAML 6 235 0 24 May 2019
Robustness to Adversarial Perturbations in Learning from Incomplete Data Amir Najafi S. Maeda Masanori Koyama Takeru Miyato OOD 32 129 0 24 May 2019
Interpreting Adversarially Trained Convolutional Neural Networks Tianyuan Zhang Zhanxing Zhu AAML GAN FAtt 28 158 0 23 May 2019
Adversarial Training and Robustness for Multiple Perturbations Florian Tramèr Dan Boneh AAML SILM 28 375 0 30 Apr 2019
Variational Inference with Latent Space Quantization for Adversarial Resilience Vinay Kyatham P. PrathoshA. Tarun Kumar Yadav Deepak Mishra Dheeraj Mundhra AAML 19 3 0 24 Mar 2019
Interpreting Neural Networks Using Flip Points Roozbeh Yousefzadeh D. O’Leary AAML FAtt 22 17 0 21 Mar 2019
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations Saeid Asgari Taghanaki Kumar Abhishek Shekoofeh Azizi Ghassan Hamarneh AAML 31 40 0 03 Mar 2019
On the Sensitivity of Adversarial Robustness to Input Data Distributions G. Ding Kry Yik-Chau Lui Xiaomeng Jin Luyu Wang Ruitong Huang OOD 26 59 0 22 Feb 2019
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples Kevin Roth Yannic Kilcher Thomas Hofmann AAML 27 175 0 13 Feb 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 22 1,998 0 08 Feb 2019
Adversarial Examples Are a Natural Consequence of Test Error in Noise Nic Ford Justin Gilmer Nicholas Carlini E. D. Cubuk AAML 36 318 0 29 Jan 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 34 721 0 28 Jan 2019
Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples Kamil Nar Orhan Ocal S. Shankar Sastry Kannan Ramchandran AAML 27 54 0 24 Jan 2019
The Limitations of Adversarial Training and the Blind-Spot Attack Huan Zhang Hongge Chen Zhao Song Duane S. Boning Inderjit S. Dhillon Cho-Jui Hsieh AAML 22 144 0 15 Jan 2019
Adversarial Robustness May Be at Odds With Simplicity Preetum Nakkiran AAML 14 105 0 02 Jan 2019
A Spectral View of Adversarially Robust Features Shivam Garg Vatsal Sharan B. Zhang Gregory Valiant AAML 19 21 0 15 Nov 2018
Theoretical Analysis of Adversarial Learning: A Minimax Approach Zhuozhuo Tu Jingwei Zhang Dacheng Tao AAML 21 68 0 13 Nov 2018
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning K. Makarychev Pascal Dupré Yury Makarychev Giancarlo Pellegrino Dan Boneh AAML 29 64 0 08 Nov 2018
Excessive Invariance Causes Adversarial Vulnerability J. Jacobsen Jens Behrmann R. Zemel Matthias Bethge AAML 33 166 0 01 Nov 2018
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network Xuanqing Liu Yao Li Chongruo Wu Cho-Jui Hsieh AAML OOD 24 171 0 01 Oct 2018
A Kernel Perspective for Regularizing Deep Neural Networks A. Bietti Grégoire Mialon Dexiong Chen Julien Mairal 16 15 0 30 Sep 2018
Generalization Error in Deep Learning Daniel Jakubovitz Raja Giryes M. Rodrigues AI4CE 32 109 0 03 Aug 2018
Adversarial examples from computational constraints Sébastien Bubeck Eric Price Ilya P. Razenshteyn AAML 65 230 0 25 May 2018
Towards the first adversarially robust neural network model on MNIST Lukas Schott Jonas Rauber Matthias Bethge Wieland Brendel AAML OOD 14 369 0 23 May 2018
On Visual Hallmarks of Robustness to Adversarial Malware Alex Huang Abdullah Al-Dujaili Erik Hemberg Una-May O’Reilly AAML 33 7 0 09 May 2018
Robustness via Deep Low-Rank Representations Amartya Sanyal Varun Kanade Philip Torr P. Dokania OOD 27 16 0 19 Apr 2018