Generative attention adversarial classification network for unsupervised domain adaptation

Wendong Chen, Haifeng Hu

Abstract: Highlights•We propose an approach to solve the unsupervised domain adaptation.•We provide an improved generative adversarial network following the feature extractor F to learn a joint feature distribution between source and target domains.•We present an attention module in the process of adversarial learning, which allows the discriminator to distinguish the transferable regions among the source and target images.•We propose the simple and efficient method of giving unlabeled target domain pseudo labels, which helps us obtain a part of the category information of target domain data and can improve the performance of our model and mitigate negative transfer at the same time.•Experiments demonstrate that our model achieves excellent result s on several standard domain adaptation datasets.