Unsupervised domain adaptation addresses the problem of classifying data in an unlabeled target domain, given labeled source that share a common label space but follow different distribution. Most recent methods take approach explicitly aligning feature distributions between two domains. Differently, motivated by fundamental assumption for adaptability, we re-cast as discriminative clustering d...

Recently, in order to address the unsupervised domain adaptation (UDA) problem, extensive studies have been proposed achieve transferrable models. Among them, most prevalent method is adversarial adaptation, which can shorten distance between source and target domain. Although learning very effective, it still leads instability of network drawbacks confusing category information. In this paper,...

This paper studies a new, practical but challenging problem, called Class-Incremental Unsupervised Domain Adaptation (CI-UDA), where the labeled source domain contains all classes, classes in unlabeled target increase sequentially. problem is due to two difficulties. First, and label sets are inconsistent at each time step, which makes it difficult conduct accurate alignment. Second, previous u...