Augmentation-Guided Adversarial Training via Local Stability-Based Sample Selection
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arXiv:2610.33331v1 Announce Type: new Abstract: Adversarial training remains one of the most effective defenses against adversarial attacks, but its standard PGD-based training is computationally expensive because adversarial examples are generated for every training sample. Selective adversarial t…
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- 2026-10-27 04:00 · arXiv cs.LG
Augmentation-Guided Adversarial Training via Local Stability-Based Sample Selection