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

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

RANK_REASON Academic paper analyzing adversarial training for Vision Transformers.

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• Vision Transformers
• CNNs
• Hugging Face

• paper
• safety