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arXiv · 2312.16339

Universal Pyramid Adversarial Training for Improved ViT Performance

Ping-yeh Chiang·Yipin Zhou·Omid Poursaeed·Satya Narayan Shukla·Ashish Shah·Tom Goldstein·Ser-Nam Lim
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Abstract

Recently, Pyramid Adversarial training (Herrmann et al., 2022) has been shown to be very effective for improving clean accuracy and distribution-shift robustness of vision transformers. However, due to the iterative nature of adversarial training, the technique is up to 7 times more expensive than standard training. To make the method more efficient, we propose Universal Pyramid Adversarial training, where we learn a single pyramid adversarial pattern shared across the whole dataset instead of the sample-wise patterns. With our proposed technique, we decrease the computational cost of Pyramid Adversarial training by up to 70% while retaining the majority of its benefit on clean performance and distribution-shift robustness. In addition, to the best of our knowledge, we are also the first to find that universal adversarial training can be leveraged to improve clean model performance.

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Ping-yeh Chiang, Yipin Zhou, Omid Poursaeed, Satya Narayan Shukla, Ashish Shah, Tom Goldstein, Ser-Nam Lim. 2023-12-26. Universal Pyramid Adversarial Training for Improved ViT Performance. https://arxiv.org/abs/2312.16339

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