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.

域自适应算法是一种功能强大的工具，用于传递源域的知识并为目标任务提供足够的注释。最近，对抗性学习被嵌入到深度网络中，以减少源域和目标域之间的域转移，以学习可转移的功能。现有的对抗域自适应方法旨在减小源域和目标域之间的差异，而忽略源域和目标域之间的类差异。本文提出了一种用于无监督域自适应的新型多重对抗网络（MAN）。MAN利用一对分类器来最大程度地减少域间差异，并为每个类别嵌入一个域标识符，以实现类内差异。此外，我们通过使用功能规范术语来规范特定于任务的功能，从而将MAN扩展为改进的MAN（iMAN），这可以改善模型的通用性，并有助于最大程度地减少类内差异。我们在两个真实的数据集Office–Home和ImageCLEF-DA上进行了广泛的实验，实验结果表明，与几种最新的无监督域自适应方法相比，我们的方法是有效和优越的。