  Semi-supervised domain adaptation (SSDA) is to adapt a learner to a new
domain with only a small set of labeled samples when a large labeled dataset is
given on a source domain. In this paper, we propose a pair-based SSDA method
that adapts a model to the target domain using self-distillation with sample
pairs. Each sample pair is composed of a teacher sample from a labeled dataset
(i.e., source or labeled target) and its student sample from an unlabeled
dataset (i.e., unlabeled target). Our method generates an assistant feature by
transferring an intermediate style between the teacher and the student, and
then train the model by minimizing the output discrepancy between the student
and the assistant. During training, the assistants gradually bridge the
discrepancy between the two domains, thus allowing the student to easily learn
from the teacher. Experimental evaluation on standard benchmarks shows that our
method effectively minimizes both the inter-domain and intra-domain
discrepancies, thus achieving significant improvements over recent methods.
