  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.
