Augmentation-guided adversarial training uses local stability-based sample selection to reduce computation in selective adversarial training.
Read the original at arxiv.org→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...
Original headline: "Augmentation-Guided Adversarial Training via Local Stability-Based Sample Selection"
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- Oct 27, 04:00 UTC arXiv cs.LG lead source Augmentation-Guided Adversarial Training via Local Stability-Based Sample Selection