  We study the problem of unsupervised domain adaption in the universal
scenario, in which only some of the classes are shared between the source and
target domains. We present a scoring scheme that is effective in identifying
the samples of the shared classes. The score is used to select which samples in
the target domain to pseudo-label during training. Another loss term encourages
diversity of labels within each batch. Taken together, our method is shown to
outperform, by a sizable margin, the current state of the art on the literature
benchmarks.
