  Unsupervised domain adaptation seeks to learn an invariant and discriminative
representation for an unlabeled target domain by leveraging the information of
a labeled source dataset. We propose to improve the discriminative ability of
the target domain representation by simultaneously learning tightly clustered
target representations while encouraging that each cluster is assigned to a
unique and different class from the source. This strategy alleviates the
effects of negative transfer when combined with adversarial domain matching
between source and target representations. Our approach is robust to
differences in the source and target label distributions and thus applicable to
both balanced and imbalanced domain adaptation tasks, and with a simple
extension, it can also be used for partial domain adaptation. Experiments on
several benchmark datasets for domain adaptation demonstrate that our approach
can achieve state-of-the-art performance in all three scenarios, namely,
balanced, imbalanced and partial domain adaptation.
