Debiased Prototype Network for Adversarial Domain Adaptation

Domain adaptation is an important and challenging task. Existing adversarial domain adaptation methods explore the relationship between the source and target domains, with the knowledge learned in the source domain supporting the target domain task. The quality of the knowledge will affect the task performance of the transfer, i.e., the higher the quality of the knowledge, the better the transfer task performance. To obtain better domain-invariant knowledge, we extract domain-invariant semantic information over the unit sphere via the prototype network. With the help of geometric constraints from the hypersphere, the features can be more tightly clustered with the estimated prototype (representatives of each class). Adaptation is achieved by adversarial learning to align the domain distribution, which enhances the transferability of the learned features and obtains the basic prototype. Since the basic prototypes dominantly computed from the source domain are biased against the expected domain-invariant prototype, a debiased method is further proposed to obtain the domain-invariant prototypes. Specifically, our method diminishes the intra- and inter- class bias to achieve the class-level alignment. Extensive experiments demonstrate that our model achieves state-of-the-art performance on several domain adaptation benchmark datasets. Our code is available at https://github.com/Chunweiwu-source/DPN.