  As a more practical setting for unsupervised domain adaptation, Universal
Domain Adaptation (UDA) is recently introduced, where the target label set is
unknown. One of the big challenges in UDA is how to determine the common label
set shared by source and target domains, as there is simply no labeling
available in the target domain. In this paper, we employ a probabilistic
approach for locating the common label set, where each source class may come
from the common label set with a probability. In particular, we propose a novel
approach for evaluating the probability of each source class from the common
label set, where this probability is computed by the prediction margin
accumulated over the whole target domain. Then, we propose a simple universal
adaptation network (S-UAN) by incorporating the probabilistic structure for the
common label set. Finally, we analyse the generalization bound focusing on the
common label set and explore the properties on the target risk for UDA.
Extensive experiments indicate that S-UAN works well in different UDA settings
and outperforms the state-of-the-art methods by large margins.
