  We extend semi-supervised learning to the problem of domain adaptation to
learn significantly higher-accuracy models that train on one data distribution
and test on a different one. With the goal of generality, we introduce
AdaMatch, a method that unifies the tasks of unsupervised domain adaptation
(UDA), semi-supervised learning (SSL), and semi-supervised domain adaptation
(SSDA). In an extensive experimental study, we compare its behavior with
respective state-of-the-art techniques from SSL, SSDA, and UDA on vision
classification tasks. We find AdaMatch either matches or significantly exceeds
the state-of-the-art in each case using the same hyper-parameters regardless of
the dataset or task. For example, AdaMatch nearly doubles the accuracy compared
to that of the prior state-of-the-art on the UDA task for DomainNet and even
exceeds the accuracy of the prior state-of-the-art obtained with pre-training
by 6.4% when AdaMatch is trained completely from scratch. Furthermore, by
providing AdaMatch with just one labeled example per class from the target
domain (i.e., the SSDA setting), we increase the target accuracy by an
additional 6.1%, and with 5 labeled examples, by 13.6%.
