Category-Level Adversarial Self-Ensembling for Domain Adaptation

Domain adaptation aims at learning from a source data distribution a well-performing model on a different target data distribution. Recently, the self-ensembling-based methods have been proved to be effective for unsupervised domain adaptation. However, they still have two shortcomings: 1) no explicitly constraint about the distributions between the source and target domains; 2) the Euclidean distance fails to measure the similarity between two distributions with no overlap. To solve those shortcomings, we propose a novel Category-level Adversarial Self-ensembling (CAS) model for domain adaptation, which contains two types of consistency constraints. The first one is how to constrain the descriptions for source and target domains to be aligned. Therefore, we adopt a minimax game with a discrepancy loss between the category information generated by two classifiers. As the self-ensembling consists of two sub-networks: student and teacher networks, the second one is the consistency between those two networks for the target samples. Aiming to overcome the disadvantage of the Euclidean metric, we employ the Wasserstein distance to measure the difference between two probabilistic distributions. Experiments on several benchmarks demonstrate that our proposed CAS is superior to existing methods.