Cross-domain Mutual Information Adversarial Maximization

Domain adaptation challenges the problem where the source domain and the target domain have distinctive data distributions. Different from previous approaches which align the two domains by minimizing a distribution metric, in this paper, we report a new perspective of handling unsupervised domain adaptation. Specifically, we formulate domain adaptation as maximizing the obtained knowledge of the target domain through observing the source domain. Technically, we maximize the mutual information between the source domain features and the target domain features in a deep adversarial network. Firstly, we use a feature extraction network and a domain discriminator with opposite goals to form adversarial components, and learn the domain-invariant features between the source and target domains through adversarial training. Secondly, we use the optimization goal of maximizing the mutual information between cross-domain features to supervise the adversarial training process to ensure that the maximum target domain information can be obtained by observing the source domain features. Finally, we evaluate our method on four datasets: Office-31, ImageCLEF-DA, Office-Home, and VisDA-2017, and all achieve better performance than previous methods. We show that our method, named Cross-domain Mutual Information Adversarial Maximization (CMIAM), is a promising approach and able to outperform previous state-of-the-arts on various unsupervised domain adaptation tasks.