Data Augmentation Alone Can Improve Adversarial Training

24 January 2023

Papers citing "Data Augmentation Alone Can Improve Adversarial Training"

32 / 32 papers shown

Title
ZeroPur: Succinct Training-Free Adversarial Purification Xiuli Bi Zonglin Yang Bo Liu Xiaodong Cun Chi-Man Pun 75 0 0 05 Jun 2024
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization Lin Li Michael W. Spratling AAML 66 35 0 09 Dec 2022
Understanding Robust Overfitting of Adversarial Training and Beyond Chaojian Yu Bo Han Li Shen Jun Yu Chen Gong Biwei Huang Tongliang Liu OOD 61 60 0 17 Jun 2022
Data Augmentation Can Improve Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 53 285 0 09 Nov 2021
AugMax: Adversarial Composition of Random Augmentations for Robust Training Haotao Wang Chaowei Xiao Jean Kossaifi Zhiding Yu Anima Anandkumar Zhangyang Wang 66 110 0 26 Oct 2021
Improving Robustness using Generated Data Sven Gowal Sylvestre-Alvise Rebuffi Olivia Wiles Florian Stimberg D. A. Calian Timothy A. Mann 66 299 0 18 Oct 2021
Exploring Memorization in Adversarial Training Yinpeng Dong Ke Xu Xiao Yang Tianyu Pang Zhijie Deng Hang Su Jun Zhu TDI 51 73 0 03 Jun 2021
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? Vikash Sehwag Saeed Mahloujifar Tinashe Handina Sihui Dai Chong Xiang M. Chiang Prateek Mittal OOD 76 129 0 19 Apr 2021
TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation Samuel G. Müller Frank Hutter ViT MQ 49 287 0 18 Mar 2021
Consistency Regularization for Adversarial Robustness Jihoon Tack Sihyun Yu Jongheon Jeong Minseon Kim Sung Ju Hwang Jinwoo Shin AAML 61 61 0 08 Mar 2021
Low Curvature Activations Reduce Overfitting in Adversarial Training Vasu Singla Sahil Singla David Jacobs Soheil Feizi AAML 56 47 0 15 Feb 2021
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 47 331 0 07 Oct 2020
Torchattacks: A PyTorch Repository for Adversarial Attacks Hoki Kim 68 204 0 24 Sep 2020
Understanding and Improving Fast Adversarial Training Maksym Andriushchenko Nicolas Flammarion AAML 68 288 0 06 Jul 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 211 1,837 0 03 Mar 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 94 800 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 70 69 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 61 64 0 11 Feb 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 361 42,299 0 03 Dec 2019
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 119 751 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 68 333 0 31 May 2019
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features Sangdoo Yun Dongyoon Han Seong Joon Oh Sanghyuk Chun Junsuk Choe Y. Yoo OOD 604 4,766 0 13 May 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 67 726 0 28 Jan 2019
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Michael I. Jordan 127 2,542 0 24 Jan 2019
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 131 789 0 30 Apr 2018
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 269 9,743 0 25 Oct 2017
Random Erasing Data Augmentation Zhun Zhong Liang Zheng Guoliang Kang Shaozi Li Yi Yang 90 3,630 0 16 Aug 2017
Improved Regularization of Convolutional Neural Networks with Cutout Terrance Devries Graham W. Taylor 107 3,758 0 15 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 269 12,029 0 19 Jun 2017
Wide Residual Networks Sergey Zagoruyko N. Komodakis 312 7,971 0 23 May 2016
Identity Mappings in Deep Residual Networks Kaiming He Xinming Zhang Shaoqing Ren Jian Sun 330 10,172 0 16 Mar 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.9K 193,426 0 10 Dec 2015