Unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) are two typical strategies to reduce expensive manual annotations in machine learning. In order to learn effective models for a target task, UDA utilizes the available labeled source data, which may have different distributions from unlabeled samples in the target domain, while SSL employs few manually annotated target samples. Although UDA and SSL are seemingly very different strategies, we find that they are closely related in terms of task objectives and solutions, and SSL is a special case of UDA problems. Based on this finding, we further investigate whether SSL methods work on UDA tasks. By adapting eight representative SSL algorithms on UDA benchmarks, we show that SSL methods are strong UDA learners. Especially, state-of-the-art SSL methods significantly outperform existing UDA methods on the challenging UDA benchmark of DomainNet, and state-of-the-art UDA methods could be further enhanced with SSL techniques. We thus promote that SSL methods should be employed as baselines in future UDA studies and expect that the revealed relationship between UDA and SSL could shed light on future UDA development. Codes are available at \url{https://github.com/YBZh}.
翻译:无人监督的域适应(UDA)和半监督的域适应(SSL)是减少机器学习中昂贵的人工说明(SSL)的两种典型战略。为了了解目标任务的有效模式,UDA使用现有的标签源数据,这些数据可能与目标领域的无标签样本有不同的分布,而SSL则使用很少人工附加说明的目标样本。虽然UDA和SSL似乎是非常不同的战略,但我们发现它们与任务目标和解决办法密切相关,SSL是UDA问题的一个特例。基于这一发现,我们进一步调查SSL是否采用UDA任务的方法。我们通过在UDA基准上修改八种具有代表性的SSL算法,我们表明SSL方法与无标签样本的UDA学习者是强大的UDA。特别是,最先进的SSL方法大大优于UDA基准方面现有的UDA方法,而采用最先进的UDA方法可以进一步加强SLA技术。我们因此提倡在今后UDA研究中使用SSL方法作为基准,并期望ULA/SLADA/SLDA的光源发展关系。