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Most existing unsupervised domain adaptation methods mainly focused onaligning the marginal distributions of samples between the source and targetdomains. This setting does not sufficiently consider the class distributioninformation between the two domains, which could adversely affect the reductionof domain gap. To address this issue, we propose a novel approach calledConditional ADversarial Image Translation (CADIT) to explicitly align the classdistributions given samples between the two domains. It integrates adiscriminative structure-preserving loss and a joint adversarial generationloss. The former effectively prevents undesired label-flipping during the wholeprocess of image translation, while the latter maintains the joint distributionalignment of images and labels. Furthermore, our approach enforces theclassification consistence of target domain images before and after adaptationto aid the classifier training in both domains. Extensive experiments wereconducted on multiple benchmark datasets including Digits, Faces, Scenes andOffice31, showing that our approach achieved superior classification in thetarget domain when compared to the state-of-the-art methods. Also, bothqualitative and quantitative results well supported our motivation thataligning the class distributions can indeed improve domain adaptation.

Class Distribution Alignment for Adversarial Domain Adaptation