WAT: Improve the Worst-class Robustness in Adversarial Training

WAT: Improve the Worst-class Robustness in Adversarial Training

8 February 2023

Papers citing "WAT: Improve the Worst-class Robustness in Adversarial Training"

17 / 17 papers shown

Title
Enhancing Robust Fairness via Confusional Spectral Regularization Gaojie Jin Sihao Wu Jiaxu Liu Tianjin Huang Ronghui Mu 186 1 0 22 Jan 2025
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training Tejaswini Medi Steffen Jung Margret Keuper AAML 92 3 0 30 Oct 2024
Analysis and Applications of Class-wise Robustness in Adversarial Training Qi Tian Kun Kuang Ke Jiang Leilei Gan Yisen Wang AAML 58 47 0 29 May 2021
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy Philipp Benz Chaoning Zhang Adil Karjauv In So Kweon AAML 65 59 0 26 Oct 2020
To be Robust or to be Fair: Towards Fairness in Adversarial Training Han Xu Xiaorui Liu Yaxin Li Anil K. Jain Jiliang Tang 46 180 0 13 Oct 2020
Do Wider Neural Networks Really Help Adversarial Robustness? Boxi Wu Jinghui Chen Deng Cai Xiaofei He Quanquan Gu AAML 62 95 0 03 Oct 2020
Bag of Tricks for Adversarial Training Tianyu Pang Xiao Yang Yinpeng Dong Hang Su Jun Zhu AAML 78 269 0 01 Oct 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 216 1,846 0 03 Mar 2020
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 127 752 0 31 May 2019
VC Classes are Adversarially Robustly Learnable, but Only Improperly Omar Montasser Steve Hanneke Nathan Srebro 35 139 0 12 Feb 2019
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 104 1,781 0 30 May 2018
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 307 12,069 0 19 Jun 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 266 8,555 0 16 Aug 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 340 7,985 0 23 May 2016
Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data Salman H. Khan Munawar Hayat Bennamoun Ferdous Sohel R. Togneri 72 882 0 14 Aug 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 277 19,066 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 270 14,927 1 21 Dec 2013