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Apr, 2024
通过C-Flat增强持续学习
Make Continual Learning Stronger via C-Flat
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Ang Bian, Wei Li, Hangjie Yuan, Chengrong Yu, Zixiang Zhao...
TL;DR
通过连续学习方法中的权重损失景观锐度最小化,本研究提出了一种适用于连续学习任务的C-Flat方法,可以在几乎所有情况下提高模型性能。
Abstract
model generalization
ability upon incrementally acquiring dynamically updating knowledge from sequentially arriving tasks is crucial to tackle the sensitivity-stability dilemma in
continual learning
(CL).
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