Cited by 205Adversarial Robustness in Machine LearningMachine Learning and Data ClassificationDomain Adaptation and Few-Shot Learning

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

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Chen, Tianlong, Liu, Sijia, Chang, Shiyu, Cheng, Yu, Amini, Lisa, Wang, Zhangyang·2020·2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr42600.2020.00078

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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 pretrained models for the first time. We find these robust pretrained 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 pretrained models have 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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Authors: Chen, T., Liu, S., Chang, S., Cheng, Y., Amini, L., & Wang, Z.
Affiliations: Texas A&M University (US)
IBM (United States) (US)
Microsoft (United States) (US)
ORCID: Chen, Tianlong
Liu, Sijia
Chang, Shiyu
Cheng, Yu
Amini, Lisa
Wang, Zhangyang
Venue: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher: IEEE
Published: 2020-06
Pages: 696-705
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr42600.2020.00078
OpenAlex: W3035106315
Cited by: 205
References: 46
Citations by year: 2026: 13 · 2025: 32 · 2024: 35 · 2023: 34 · 2022: 31 · 2021: 46 · 2020: 14
FWCI: 11.94
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: adversarial robustness, self-supervised pre-training, adversarial fine-tuning, adversarial vulnerability, robust accuracy, CIFAR-10, computational complexity reduction

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

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