BriefGPT.xyz
Jul, 2020
对抗领域适应的双 Mixup 正则化学习
Dual Mixup Regularized Learning for Adversarial Domain Adaptation
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Yuan Wu, Diana Inkpen, Ahmed El-Roby
TL;DR
提出了一种基于双混合正则化学习(DMRL)的无监督域自适应方法,采用判别器辅助分类器训练,通过混合正则化促进模型对于样本和特征的分类和对于域变量的鲁棒性,实验结果表明该方法在四个域适应基准上实现了最先进结果。
Abstract
Recent advances on
unsupervised domain adaptation
(UDA) rely on
adversarial learning
to disentangle the explanatory and transferable features for domain adaptation. However, there are two issues with the existing
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