Generative attention adversarial classification network for unsupervised domain adaptation

Abstract

Abstract Domain adaptation is a significant and popular issue of solving distribution discrepancy among different domains in computer vision. Generally, previous works proposed are mainly devoted to reducing domain shift between source domain with labeled data and target domain without labels. Adversarial learning in deep networks has already been widely applied to learn disentangled and transferable features between two different domains to minimize domains distribution discrepancy. However, these methods rarely consider class distributions among source data during adversarial learning, and they pay little attention to these transferable regions among source and target domains images. In this paper, we propose a Generative Attention Adversarial Classification Network (GAACN) model for unsupervised domain adaptation. To learn a joint feature distribution between source and target domains, we present an improved generative adversarial network (GAN) following the feature extractor. Firstly, the discriminator of GAN discriminates the distribution of domains and the classes distribution among source data during adversarial learning, so that our feature extractor can learn a joint feature distribution between source and target domains and maintain the classes consistent simultaneously. Secondly, we present an attention module embedded in GAN, which allows the discriminator to discriminate the transferable regions among the images of source and target domains. Lastly, we propose a simple and efficient method which allocates pseudo-labels for unlabeled target data, and it can improve the performance of our model GAACN while mitigating negative transfer. Extensive experiments demonstrate that our proposed model achieves perfect results on several standard domain adaptation datasets.