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We extend semi-supervised learning to the problem of domain adaptation tolearn significantly higher-accuracy models that train on one data distributionand test on a different one. With the goal of generality, we introduceAdaMatch, 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 withrespective state-of-the-art techniques from SSL, SSDA, and UDA on visionclassification tasks. We find AdaMatch either matches or significantly exceedsthe state-of-the-art in each case using the same hyper-parameters regardless ofthe dataset or task. For example, AdaMatch nearly doubles the accuracy comparedto that of the prior state-of-the-art on the UDA task for DomainNet and evenexceeds the accuracy of the prior state-of-the-art obtained with pre-trainingby 6.4% when AdaMatch is trained completely from scratch. Furthermore, byproviding AdaMatch with just one labeled example per class from the targetdomain (i.e., the SSDA setting), we increase the target accuracy by anadditional 6.1%, and with 5 labeled examples, by 13.6%.

AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation