Sample Selection for Universal Domain Adaptation

This paper studies the problem of unsupervised domain adaption in universal scenario, which only some classes are shared between source and target domains. We present a scoring scheme that is effective identifying samples classes. The score used to select for apply specific losses during training; pseudo-labels high confidence regularization low samples. Taken together, our method shown outperform, by sizeable margin, current state art on literature benchmarks.