Benign overfitting in adversarial training boosts Vision Transformer robustness

Researchers have theoretically analyzed adversarial training for Vision Transformers (ViTs), finding it can achieve near-zero robust training loss and generalization error under specific conditions. This defense strategy, previously observed in CNNs, helps ViTs maintain strong generalization even when overfitting occurs, a phenomenon termed benign overfitting. Experiments on synthetic and real-world datasets support these theoretical conclusions. AI

影响 Provides theoretical grounding for adversarial training in ViTs, potentially improving their robustness against adversarial attacks.

排序理由 Academic paper analyzing adversarial training for Vision Transformers.

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