Paper Info
Title
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness.
Abstract
Adversarial training (AT) for robust representation learning and self-supervised learning (SSL) for unsupervised representation learning are two active research fields. Integrating AT into SSL, multiple prior works have accomplished a highly significant yet challenging task: learning robust representation without labels. A widely used framework is adversarial contrastive learning which couples AT and SSL, and thus constitutes a very complex optimization problem. Inspired by the divide-and-conquer philosophy, we conjecture that it might be simplified as well as improved by solving two sub-problems: non-robust SSL and pseudo-supervised AT. This motivation shifts the focus of the task from seeking an optimal integrating strategy for a coupled problem to finding sub-solutions for sub-problems. With this said, this work discards prior practices of directly introducing AT to SSL frameworks and proposed a two-stage framework termed Decoupled Adversarial Contrastive Learning (DeACL). Extensive experimental results demonstrate that our DeACL achieves SOTA self-supervised adversarial robustness while significantly reducing the training time, which validates its effectiveness and efficiency. Moreover, our DeACL constitutes a more explainable solution, and its success also bridges the gap with semi-supervised AT for exploiting unlabeled samples for robust representation learning. The code is publicly accessible at https://github.com/pantheon5100/DeACL.
Year
DOI
Venue
2022
10.1007/978-3-031-20056-4_42
European Conference on Computer Vision
Keywords
DocType
Citations 
Adversarial contrastive learning,Adversarial training,Self-supervised learning,Adversarial robustness
Conference
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Chaoning Zhang126.82
Kang Zhang200.68
Chenshuang Zhang300.68
Axi Niu400.68
Jiu Feng500.34
Chang D. Yoo637545.88
In So Kweon72795207.62