Adversarial Domain Alignment Feature Similarity Enhancement Learning for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) attempts to transfer knowledge learned from labeled source domain to unlabeled target domain. Its main challenge is distribution gap between two domains. Most of works focus on reducing domain shift by domain alignment methods. Although these methods can reduce the domain shift, the samples far from the class center of target domain are still easily misclassified. To solve the problem, we propose a new approach named Adversarial Domain Alignment Feature Similarity Enhancement Learning (AASE) It learns domain invariant features by adversarial game and correlation alignment to reduce the domain gap, and makes these features having better discrimination via joint central discrimination and feature similarity enhancement. AASE makes the learned features have better intra class compactness and inter class separability. AASE is evaluated on two datasets, and the results show that AASE has critical improvement in the performance of UDA.
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