  Most research on domain adaptation has focused on the purely unsupervised
setting, where no labeled examples in the target domain are available. However,
in many real-world scenarios, a small amount of labeled target data is
available and can be used to improve adaptation. We address this
semi-supervised setting and propose to use dynamic feature alignment to address
both inter- and intra-domain discrepancy. Unlike previous approaches, which
attempt to align source and target features within a mini-batch, we propose to
align the target features to a set of dynamically updated class prototypes,
which we use both for minimizing divergence and pseudo-labeling. By updating
based on class prototypes, we avoid problems that arise in previous approaches
due to class imbalances. Our approach, which doesn't require extensive tuning
or adversarial training, significantly improves the state of the art for
semi-supervised domain adaptation. We provide a quantitative evaluation on two
standard datasets, DomainNet and Office-Home, and performance analysis.
