BriefGPT.xyz
Dec, 2023
分类增量学习的对抗鲁棒性
Class Incremental Learning for Adversarial Robustness
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Seungju Cho, Hongshin Lee, Changick Kim
TL;DR
提出了一种将增量学习与敌对训练相结合的方法,通过引入FPD损失函数和LAD损失函数解决了增量学习中的鲁棒性问题,并在实验中证明了其比现有方法更为优越。
Abstract
adversarial training
integrates adversarial examples during model training to enhance
robustness
. However, its application in fixed dataset settings differs from real-world dynamics, where data accumulates increm
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