Joint category-level and discriminative feature learning networks for unsupervised domain adaptation

Abstract

Unsupervised domain adaptation (UDA) aims to build a classifier for the unlabeled target domain by transferring knowledge from a well-labeled source domain. Recently deep domain adaptation methods can not effectively integrate discriminability with transferability of features, and these methods can only reduce, but not remove, the cross-domain discrepancy. To this end, this paper proposes a new domain adaptation method called Joint Category-Level and Discriminative Feature Learning Network (CDN). CDN not only achieves domain adaptation by minimizing category-level distribution discrepancy between domains but also learns discriminative feature representations via maximizing inter-category distance and selecting transferability samples simultaneously. Moreover, we develop a Transferability Weighting Module (TWM), which is based on a constructed classifier, to further strengthen the discriminability of sample’s features. The experimental results demonstrate that CDN can significantly decrease the cross-domain distribution inconsistency and further promote the classification performance.