Semi-Supervised Domain Adaptation (SSDA) is a recently emerging research topic that extends from the widely-investigated Unsupervised Domain Adaptation (UDA) by further having a few target samples labeled, i.e., the model is trained with labeled source samples, unlabeled target samples as well as a few labeled target samples. Compared with UDA, the key to SSDA lies how to most effectively utilize the few labeled target samples. Existing SSDA approaches simply merge the few precious labeled target samples into vast labeled source samples or further align them, which dilutes the value of labeled target samples and thus still obtains a biased model. To remedy this, in this paper, we propose to decouple SSDA as an UDA problem and a semi-supervised learning problem where we first learn an UDA model using labeled source and unlabeled target samples and then adapt the learned UDA model in a semi-supervised way using labeled and unlabeled target samples. By utilizing the labeled source samples and target samples separately, the bias problem can be well mitigated. We further propose a consistency learning based mean teacher model to effectively adapt the learned UDA model using labeled and unlabeled target samples. Experiments show our approach outperforms existing methods.

翻译：半监督域适应（SSDA）是近期兴起的研究课题，它从广泛研究的无监督域适应（UDA）扩展而来，进一步包含了少量标注的目标样本，即模型使用标注的源样本、未标注的目标样本以及少量标注的目标样本进行训练。与UDA相比，SSDA的关键在于如何最有效地利用这少量标注的目标样本。现有的SSDA方法简单地将少量珍贵的标注目标样本混入大量标注源样本中，或进一步对其进行对齐，这种做法稀释了标注目标样本的价值，因此仍会得到有偏的模型。为解决此问题，本文提出将SSDA解耦为一个UDA问题和一个半监督学习问题：首先使用标注源样本和未标注目标样本学习一个UDA模型，然后以半监督方式利用标注和未标注目标样本对学得的UDA模型进行自适应。通过分别利用标注源样本和标注目标样本，可以很好地缓解偏倚问题。我们进一步提出基于一致性学习的均值教师模型，以有效利用标注和未标注目标样本对学得的UDA模型进行自适应。实验表明，我们的方法优于现有方法。