Disentanglement then reconstruction: Unsupervised domain adaptation by twice distribution alignments

Unsupervised domain adaptation aims to transfer knowledge from labeled source domain to unlabeled target domain. Traditional methods usually achieve domain adaptation by aligning the distributions between two domains once. We propose to align the distributions twice by a disentanglement and reconstruction process, named DTR (Disentanglement Then Reconstruction). Specifically, a feature extraction network shared by both source and target domains is used to obtain the original extracted features, then the domain invariant features and domain specific features are disentangled from the original extracted features. The domain distributions are explicitly aligned when disentangling domain invariant features. Based on the disentangled features, the class prototypes and domain prototypes can be estimated. Then, a reconstructor is trained by the disentangled features. By this reconstructor, we can construct prototypes in the original feature space using the corresponding class prototype and domain prototype similarly. The extracted features are forced to close the corresponding constructed prototypes. In this process, the distribution between two domains is implicitly aligned again. Experiment results on several public datasets confirm the effectiveness of our method.