Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

• URL: http://arxiv.org/abs/2003.12862v1
• Date: Sat, 28 Mar 2020 18:28:33 GMT
• Title: Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
• Authors: Tianlong Chen, Sijia Liu, Shiyu Chang, Yu Cheng, Lisa Amini and Zhangyang Wang
• Abstract summary: We introduce adversarial training into self-supervision, to provide general-purpose robust pre-trained models for the first time. We conduct extensive experiments to demonstrate that the proposed framework achieves large performance margins.
• Score: 134.15174177472807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pretrained models from self-supervision are prevalently used in fine-tuning downstream tasks faster or for better accuracy. However, gaining robustness from pretraining is left unexplored. We introduce adversarial training into self-supervision, to provide general-purpose robust pre-trained models for the first time. We find these robust pre-trained models can benefit the subsequent fine-tuning in two ways: i) boosting final model robustness; ii) saving the computation cost, if proceeding towards adversarial fine-tuning. We conduct extensive experiments to demonstrate that the proposed framework achieves large performance margins (eg, 3.83% on robust accuracy and 1.3% on standard accuracy, on the CIFAR-10 dataset), compared with the conventional end-to-end adversarial training baseline. Moreover, we find that different self-supervised pre-trained models have a diverse adversarial vulnerability. It inspires us to ensemble several pretraining tasks, which boosts robustness more. Our ensemble strategy contributes to a further improvement of 3.59% on robust accuracy, while maintaining a slightly higher standard accuracy on CIFAR-10. Our codes are available at https://github.com/TAMU-VITA/Adv-SS-Pretraining.

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