Invertible Projection and Conditional Alignment for Multi-Source Blended-Target Domain Adaptation

Multi-source domain adaptation (MSDA), which utilizes multiple source domains to align the distribution of a single target domain, is a popular and challenging setting in domain adaptation (DA). However, existing MSDA approaches are difficult to obtain sufficient target domain knowledge, which serve as the transfer object. Furthermore, the target distributions are confused in the real world, i.e., the model cannot obtain the domain labels of target domains. To tackle these problems, we consider a more realistic DA setting Multi-Source Blended-Target Domain Adaptation (MBDA) and propose an Invertible Projection and Conditional Alignment (IPCA) method. Specifically, to reduce the impact of the distribution discrepancy, we construct an invertible projection for the source and blended-target domains. Then, we adopt a projection consistency regularization to our model, which makes the model more robust on the domain-specific parts. In addition, because the labels of the blended-target domain are unseen, we introduce conditional discrepancy to obtain the domain-level discriminative information and guide the classifier to serve as the discriminator, which is suitable for MBDA settings. Extensive experiment results on the ImageCLEF-DA, Office-Home, and DomainNet datasets validate the effectiveness of our method.