@inproceedings{chen2020adversarial,
author = {Chen, Tianlong and Liu, Sijia and Chang, Shiyu and Cheng, Yu and Amini, Lisa and Wang, Zhangyang},
title = {Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning},
booktitle = {CVPR 2020},
year = {2020},
month = {June},
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 on GitHub.},
url = {http://approjects.co.za/?big=en-us/research/publication/adversarial-robustness-from-self-supervised-pre-training-to-fine-tuning/},
}