  This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet
under-investigated research topic that aims to learn a model of good
performance using unlabeled samples and a few labeled samples in the target
domain, with the help of labeled samples from a source domain. Several SSDA
methods have been proposed recently, which however fail to fully exploit the
value of the few labeled target samples. In this paper, we propose Enhanced
Categorical Alignment and Consistency Learning (ECACL), a holistic SSDA
framework that incorporates multiple mutually complementary domain alignment
techniques. ECACL includes two categorical domain alignment techniques that
achieve class-level alignment, a strong data augmentation based technique that
enhances the model's generalizability and a consistency learning based
technique that forces the model to be robust with image perturbations. These
techniques are applied on one or multiple of the three inputs (labeled source,
unlabeled target, and labeled target) and align the domains from different
perspectives. ECACL unifies them together and achieves fairly comprehensive
domain alignments that are much better than the existing methods: For example,
ECACL raises the state-of-the-art accuracy from 68.4 to 81.1 on VisDA2017 and
from 45.5 to 53.4 on DomainNet for the 1-shot setting. Our code is available at
\url{https://github.com/kailigo/pacl}.
