Multiple adversarial networks for unsupervised domain adaptation

Domain adaptation algorithm is a powerful tool for transferring the knowledge of source domain with sufficient annotations for target tasks. Recently, adversarial learning is embedded into deep networks to reduce domain shift between source and target domains for learning transferable features. Existing adversarial domain adaptation methods aim at reducing the source and target domain discrepancy ignoring the class discrepancy between source and target domains. This paper proposes a novel Multiple Adversarial Networks (MAN) for unsupervised domain adaptation. MAN utilizes a pair of classifiers to minimize inter-domain discrepancy and embeds a domain discriminator for each category for intra-class discrepancy. Furthermore, we extend our MAN as improved MAN (iMAN) by utilizing a feature norm term to regularize the task-specific features, which can improve model generalization and help for minimizing intra-class discrepancy. We conduct extensive experiments on two real-world datasets Office–Home and ImageCLEF-DA, and experiment results show the effectiveness and superiority of our methods compared with several state-of-the-art unsupervised domain adaptation methods.