arxiv
PublishedOctober 31, 2026 at 4:00 AM
Sparse Fine-Tuning for Parameter-Efficient Adversarial Training
Publisher summary· verbatim
arXiv:2610.39455v1 Announce Type: cross Abstract: Deep neural networks achieve strong performance across many tasks but remain vulnerable to adversarial perturbations. Adversarial training (AT) is one of the most effective defenses, yet it suffers from high memory cost on large models and the persis
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Originally published on arxiv ↗