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May, 2024
AdaAugment:一种无需调优的自适应数据增强方法
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation
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Suorong Yang, Peijia Li, Xin Xiong, Furao Shen, Jian Zhao
TL;DR
AdaAugment是一种无需调参的创新自适应增强方法,利用强化学习根据目标网络的实时反馈动态调整个别训练样本的增强程度,通过优化策略网络和目标网络的联合来有效适应增强程度,从而在效果和效率上一致性地优于其他最先进的数据增强方法。
Abstract
data augmentation
(DA) is widely employed to improve the generalization performance of
deep models
. However, most existing DA methods use augmentation operations with random magnitudes throughout training. While
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