Robustness to Adversarial Perturbations in Learning from Incomplete Data

24 May 2019

Papers citing "Robustness to Adversarial Perturbations in Learning from Incomplete Data"

31 / 81 papers shown

Title
Understanding Catastrophic Overfitting in Single-step Adversarial Training Hoki Kim Woojin Lee Jaewook Lee AAML 16 108 0 05 Oct 2020
Do Wider Neural Networks Really Help Adversarial Robustness? Boxi Wu Jinghui Chen Deng Cai Xiaofei He Quanquan Gu AAML 14 95 0 03 Oct 2020
Label Smoothing and Adversarial Robustness Chaohao Fu Hongbin Chen Na Ruan Weijia Jia AAML 16 12 0 17 Sep 2020
Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality Rui Gao 29 88 0 09 Sep 2020
On the Generalization Properties of Adversarial Training Yue Xing Qifan Song Guang Cheng AAML 25 32 0 15 Aug 2020
Measuring Robustness to Natural Distribution Shifts in Image Classification Rohan Taori Achal Dave Vaishaal Shankar Nicholas Carlini Benjamin Recht Ludwig Schmidt OOD 39 536 0 01 Jul 2020
ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining Jiefeng Chen Yixuan Li Xi Wu Yingyu Liang S. Jha OODD 24 135 0 26 Jun 2020
Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training Samet Oymak Talha Cihad Gulcu 24 19 0 19 Jun 2020
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data Zhun Deng Linjun Zhang Amirata Ghorbani James Zou 26 32 0 15 Jun 2020
Targeted Adversarial Perturbations for Monocular Depth Prediction A. Wong Safa Cicek Stefano Soatto AAML MDE 16 43 0 12 Jun 2020
Trade-offs between membership privacy & adversarially robust learning Jamie Hayes SILM 27 3 0 08 Jun 2020
Unique properties of adversarially trained linear classifiers on Gaussian data Jamie Hayes AAML 52 0 0 06 Jun 2020
Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers S. Melacci Gabriele Ciravegna Angelo Sotgiu Ambra Demontis Battista Biggio Marco Gori Fabio Roli 9 14 0 06 Jun 2020
Adversarial Weight Perturbation Helps Robust Generalization Dongxian Wu Shutao Xia Yisen Wang OOD AAML 22 17 0 13 Apr 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 75 99 0 20 Mar 2020
Understanding Self-Training for Gradual Domain Adaptation Ananya Kumar Tengyu Ma Percy Liang CLL TTA 28 228 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 33 397 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 37 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 51 223 0 25 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
Incorporating Unlabeled Data into Distributionally Robust Learning Charlie Frogner Sebastian Claici Edward Chien Justin Solomon OOD 19 26 0 16 Dec 2019
Adversarial Examples Improve Image Recognition Cihang Xie Mingxing Tan Boqing Gong Jiang Wang Alan Yuille Quoc V. Le AAML 42 564 0 21 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
Self-training with Noisy Student improves ImageNet classification Qizhe Xie Minh-Thang Luong Eduard H. Hovy Quoc V. Le NoLa 88 2,364 0 11 Nov 2019
Adversarial Training Can Hurt Generalization Aditi Raghunathan Sang Michael Xie Fanny Yang John C. Duchi Percy Liang 13 240 0 14 Jun 2019
Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai Tianle Cai Di He Chen Dan Kun He J. Hopcroft Liwei Wang 9 154 0 03 Jun 2019
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 45 743 0 31 May 2019
Are Labels Required for Improving Adversarial Robustness? J. Uesato Jean-Baptiste Alayrac Po-Sen Huang Robert Stanforth Alhussein Fawzi Pushmeet Kohli AAML 11 331 0 31 May 2019
Unsupervised Data Augmentation for Consistency Training Qizhe Xie Zihang Dai Eduard H. Hovy Minh-Thang Luong Quoc V. Le 61 2,290 0 29 Apr 2019
Improved Generalization Bounds for Adversarially Robust Learning Idan Attias A. Kontorovich Yishay Mansour 27 17 0 04 Oct 2018
On the Convergence of the EM Algorithm: A Data-Adaptive Analysis Chong Wu Can Yang Hongyu Zhao Ji Zhu 38 19 0 02 Nov 2016