  Unsupervised open-set domain adaptation (UODA) is a realistic problem where
unlabeled target data contain unknown classes. Prior methods rely on the
coexistence of both source and target domain data to perform domain alignment,
which greatly limits their applications when source domain data are restricted
due to privacy concerns. This paper addresses the challenging hypothesis
transfer setting for UODA, where data from source domain are no longer
available during adaptation on target domain. We introduce a method that
focuses on the semantic consistency under transformation of target data, which
is rarely appreciated by previous domain adaptation methods. Specifically, our
model first discovers confident predictions and performs classification with
pseudo-labels. Then we enforce the model to output consistent and definite
predictions on semantically similar inputs. As a result, unlabeled data can be
classified into discriminative classes coincided with either source classes or
unknown classes. Experimental results show that our model outperforms
state-of-the-art methods on UODA benchmarks.
