Improving Adversarial Robustness via Unlabeled Out-of-Domain Data

15 June 2020

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

10 / 10 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 28 0 0 26 Jan 2024
Robust Ranking Explanations Chao Chen Chenghua Guo Guixiang Ma Ming Zeng Xi Zhang Sihong Xie FAtt AAML 37 0 0 08 Jul 2023
Federated Learning without Full Labels: A Survey Yilun Jin Yang Liu Kai Chen Qian Yang FedML 17 26 0 25 Mar 2023
Reinforcement Learning with Stepwise Fairness Constraints Zhun Deng He Sun Zhiwei Steven Wu Linjun Zhang David C. Parkes FaML OffRL 43 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 33 13 0 02 Aug 2022
FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data Zhun Deng Jiayao Zhang Linjun Zhang Ting Ye Yates Coley Weijie J. Su James Zou 43 16 0 06 Jun 2022
Adversarial Training Helps Transfer Learning via Better Representations Zhun Deng Linjun Zhang Kailas Vodrahalli Kenji Kawaguchi James Zou GAN 36 53 0 18 Jun 2021
When and How Mixup Improves Calibration Linjun Zhang Zhun Deng Kenji Kawaguchi James Zou UQCV 36 67 0 11 Feb 2021
Adversarial examples from computational constraints Sébastien Bubeck Eric Price Ilya P. Razenshteyn AAML 65 230 0 25 May 2018
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,113 0 04 Nov 2016