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Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing

24 March 2023

Lin Li

Michael W. Spratling

AAML

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Papers citing "Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing"

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Title
Data Augmentation Alone Can Improve Adversarial Training Lin Li Michael W. Spratling 56 55 0 24 Jan 2023
Robust Weight Perturbation for Adversarial Training Chaojian Yu Bo Han Biwei Huang Li Shen Shiming Ge Bo Du Tongliang Liu AAML 62 36 0 30 May 2022
Self-Ensemble Adversarial Training for Improved Robustness Hongjun Wang Yisen Wang OOD AAML 51 50 0 18 Mar 2022
Improving Robustness using Generated Data Sven Gowal Sylvestre-Alvise Rebuffi Olivia Wiles Florian Stimberg D. A. Calian Timothy A. Mann 91 302 0 18 Oct 2021
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 330 703 0 19 Oct 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 55 331 0 07 Oct 2020
Geometry-aware Instance-reweighted Adversarial Training Jingfeng Zhang Jianing Zhu Gang Niu Bo Han Masashi Sugiyama Mohan Kankanhalli AAML 53 278 0 05 Oct 2020
Torchattacks: A PyTorch Repository for Adversarial Attacks Hoki Kim 71 207 0 24 Sep 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 227 1,858 0 03 Mar 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 116 808 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 56 404 0 26 Feb 2020
CAT: Customized Adversarial Training for Improved Robustness Minhao Cheng Qi Lei Pin-Yu Chen Inderjit Dhillon Cho-Jui Hsieh OOD AAML 82 117 0 17 Feb 2020
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 130 754 0 31 May 2019
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 157 795 0 30 Apr 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 243 3,194 0 01 Feb 2018
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 317 12,131 0 19 Jun 2017
Towards Principled Methods for Training Generative Adversarial Networks Martín Arjovsky M. Nault GAN 83 2,112 0 17 Jan 2017
Wide Residual Networks Sergey Zagoruyko N. Komodakis 353 8,000 0 23 May 2016
Identity Mappings in Deep Residual Networks Kaiming He Xinming Zhang Shaoqing Ren Jian Sun 354 10,196 0 16 Mar 2016
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,121 0 20 Dec 2014