Domain-invariant adversarial learning with conditional distribution alignment for unsupervised domain adaptation

摘要

Unsupervised domain adaption aims to reduce the divergence between the source domain and the target domain. The final objective is to learn domain-invariant features from both domains that get the minimised expected error on the target domain. The divergence between domains which is also called domain shift is mainly between the distributions of domains' samples. Additionally, the label shift is also a tricky challenge in domain adaptation. In this study, domain-invariant adversarial learning with conditional distribution alignment is proposed to alleviate the effect of domain shift with label shift. To obtain the domain-invariant features, the proposed method modifies adversarial auto-encoder architecture and performs semi-supervised learning to enlarge the inter-class discrepancy. The marginal distribution is aligned in the adversarial learning process of extracting domain-invariant features. Meanwhile, the label information is incorporated in this way to align the conditional distribution. The proposed work also theoretically analyses the generalisation bound of the proposed model. Finally, the proposed method is evaluated based on several domain adaptation tasks, including digit classification and object recognition, and achieves state-of-the-art performance.