BriefGPT.xyz
Sep, 2021
无监督领域自适应对抗鲁棒性
Adversarial Robustness for Unsupervised Domain Adaptation
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Muhammad Awais, Fengwei Zhou, Hang Xu, Lanqing Hong, Ping Luo...
TL;DR
本文提出一种通过利用ImageNet预训练模型中的中间表示,使得源域与目标域学习到的特征分布更加接近,从而提高无监督域适应模型在未标记数据上的对抗鲁棒性,不需要对抗干预或标签要求。
Abstract
Extensive
unsupervised domain adaptation
(UDA) studies have shown great success in practice by learning
transferable representations
across a labeled source domain and an unlabeled target domain with deep models.
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