Informative discriminator for domain adaptation

In this paper, we consider the problem of domain adaptation for multi-class classification, where we are provided a labeled set of examples in a source dataset and target dataset with no supervision. We tackle the mode collapse problem in adapting the classifier across domains. In this setting, we propose an adversarial learning-based approach using an informative discriminator. Our observation relies on the analysis that shows if the discriminator has access to all the information available, including the class structure present in the source dataset, then it can guide the transformation of features of the target set of classes to a more structured adapted space. Further, by training the informative discriminator using the more robust source samples, we are able to obtain better domain invariant features. Using this formulation, we achieve state-of-the-art results for the standard evaluation on benchmark datasets. We also provide detailed analysis, which shows that using all the labeled information results in an improved domain adaptation.