Improving Adversarial Robustness via Unlabeled Out-of-Domain Data

15 June 2020

Papers citing "Improving Adversarial Robustness via Unlabeled Out-of-Domain Data"

8 / 8 papers shown

Title
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective Yue Xing Xiaofeng Lin Qifan Song Yi Tian Xu Belinda Zeng Guang Cheng SSL 8 0 0 26 Jan 2024
Robust Ranking Explanations Chao Chen Chenghua Guo Guixiang Ma Ming Zeng Xi Zhang Sihong Xie FAtt AAML 25 0 0 08 Jul 2023
Reinforcement Learning with Stepwise Fairness Constraints Zhun Deng He Sun Zhiwei Steven Wu Linjun Zhang David C. Parkes FaML OffRL 23 11 0 08 Nov 2022
GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction Jiacheng Zhu Jielin Qiu Zhuolin Yang Douglas Weber M. Rosenberg Emerson Liu Bo-wen Li Ding Zhao OOD 20 13 0 02 Aug 2022
Adversarial Training Helps Transfer Learning via Better Representations Zhun Deng Linjun Zhang Kailas Vodrahalli Kenji Kawaguchi James Y. Zou GAN 29 52 0 18 Jun 2021
When and How Mixup Improves Calibration Linjun Zhang Zhun Deng Kenji Kawaguchi James Y. Zou UQCV 20 67 0 11 Feb 2021
Adversarial examples from computational constraints Sébastien Bubeck Eric Price Ilya P. Razenshteyn AAML 60 230 0 25 May 2018
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 256 3,108 0 04 Nov 2016