An Unsupervised Domain Adaptation Being Aware of Domain-specific and Label Information

Domain adaptation aims to leverage the knowledge in a label-rich source domain to facilitate the learning task in an unlabeled target domain with a different distribution. Adversarial-based domain adaptation methods have attracted increasing attention due to their remarkable performance. However, most methods learn the invariant features by aligning distribution between domains, while ignoring the domain-specific and label information. It will lead to unsatisfying invariant features and cause improper label alignment when there is much specific information. Therefore, in this paper, we propose an unsupervised method to learn more robust and discriminative invariant features for domain adaptation by using the specific features and label information. Specially, a separate batch normalization layer is introduced to replace the completely shared layer to capture domain-specific information, which will benefit the learning of invariant features. Then, to make the adaptation more sufficiently, a symmetric design of classifier and the corresponding adversarial training loss are used to realize domain-wise and label-wise alignment. The two steps are optimized iteratively to improve the performance of the model. The extensive experiments on three benchmark datasets have demonstrated the effectiveness of our method.