Hybrid adversarial network for unsupervised domain adaptation
Abstract
Recent advances suggest that adversarial domain adaptation has been embedding into deep neural networks to learn domain-transferable representations, which reduces distribution divergence in both the training and test samples. However, previous adversarial learning algorithms only resort to learn domain-transferable feature representation by bounding the feature distribution discrepancy cross-domain. These approaches, however, may lead to...
Paper Details
Title
Hybrid adversarial network for unsupervised domain adaptation
Published Date
Apr 1, 2020
Journal
Volume
514
Pages
44 - 55
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