Timezone: »
Deep models often fail to generalize well in test domains when the data distribution differs from that in the training domain. Among numerous approaches to address this Out-of-Distribution (OOD) generalization problem, there has been a growing surge of interest in exploiting Adversarial Training (AT) to improve OOD performance. Recent works have revealed that the robust model obtained by conducting sample-wise AT also retains transferability to biased test domains. In this paper, we empirically show that sample-wise AT has limited improvement on OOD performance. Specifically, we find that AT can only maintain performance at smaller scales of perturbation while Universal AT (UAT) is more robust to larger-scale perturbations. This provides us with clues that adversarial perturbations with universal (low dimensional) structures can enhance the robustness against large data distribution shifts that are common in OOD scenarios. Inspired by this, we propose two AT variants with low-rank structures to train OOD-robust models. Extensive experiments on DomainBed benchmark show that our proposed approaches outperform Empirical Risk Minimization (ERM) and sample-wise AT. Our code is available at https://github.com/NOVAglow646/NIPS22-MAT-and-LDAT-for-OOD.
Author Information
Qixun Wang (Peking University)
Yifei Wang (Peking University)
Hong Zhu (Huawei)
Yisen Wang (Peking University)
Related Events (a corresponding poster, oral, or spotlight)
-
2022 Poster: Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors »
Dates n/a. Room
More from the Same Authors
-
2022 Poster: When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture »
Yichuan Mo · Dongxian Wu · Yifei Wang · Yiwen Guo · Yisen Wang -
2022 Spotlight: Lightning Talks 6A-2 »
Yichuan Mo · Botao Yu · Gang Li · Zezhong Xu · Haoran Wei · Arsene Fansi Tchango · Raef Bassily · Haoyu Lu · Qi Zhang · Songming Liu · Mingyu Ding · Peiling Lu · Yifei Wang · Xiang Li · Dongxian Wu · Ping Guo · Wen Zhang · Hao Zhongkai · Mehryar Mohri · Rishab Goel · Yisen Wang · Yifei Wang · Yangguang Zhu · Zhi Wen · Ananda Theertha Suresh · Chengyang Ying · Yujie Wang · Peng Ye · Rui Wang · Nanyi Fei · Hui Chen · Yiwen Guo · Wei Hu · Chenglong Liu · Julien Martel · Yuqi Huo · Wu Yichao · Hang Su · Yisen Wang · Peng Wang · Huajun Chen · Xu Tan · Jun Zhu · Ding Liang · Zhiwu Lu · Joumana Ghosn · Shanshan Zhang · Wei Ye · Ze Cheng · Shikun Zhang · Tao Qin · Tie-Yan Liu -
2022 Spotlight: How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders »
Qi Zhang · Yifei Wang · Yisen Wang -
2022 Spotlight: When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture »
Yichuan Mo · Dongxian Wu · Yifei Wang · Yiwen Guo · Yisen Wang -
2022 Spotlight: Lightning Talks 1B-3 »
Chaofei Wang · Qixun Wang · Jing Xu · Long-Kai Huang · Xi Weng · Fei Ye · Harsh Rangwani · shrinivas ramasubramanian · Yifei Wang · Qisen Yang · Xu Luo · Lei Huang · Adrian G. Bors · Ying Wei · Xinglin Pan · Sho Takemori · Hong Zhu · Rui Huang · Lei Zhao · Yisen Wang · Kato Takashi · Shiji Song · Yanan Li · Rao Anwer · Yuhei Umeda · Salman Khan · Gao Huang · Wenjie Pei · Fahad Shahbaz Khan · Venkatesh Babu R · Zenglin Xu -
2022 Poster: How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders »
Qi Zhang · Yifei Wang · Yisen Wang -
2021 Poster: Dissecting the Diffusion Process in Linear Graph Convolutional Networks »
Yifei Wang · Yisen Wang · Jiansheng Yang · Zhouchen Lin -
2021 Poster: Residual Relaxation for Multi-view Representation Learning »
Yifei Wang · Zhengyang Geng · Feng Jiang · Chuming Li · Yisen Wang · Jiansheng Yang · Zhouchen Lin -
2020 Poster: Adversarial Weight Perturbation Helps Robust Generalization »
Dongxian Wu · Shu-Tao Xia · Yisen Wang