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%.

介绍AdaMatch方法来统一无监督域自适应（UDA）、半监督学习（SSL）和半监督域自适应（SSDA）的任务，实现在一个数据分布上训练，在另一个数据分布上测试具有极高准确性的模型。实验结果表明，使用AdaMatch方法，无论是在哪个数据集或任务上，有标签还是无标签，其结果都能匹配或明显超过SSL、SSDA和UDA领域最先进的技术，而且仅使用相同的超参数即可。

AdaMatch：一种半监督学习和域适应的统一方法