  While unsupervised domain adaptation has been explored to leverage the
knowledge from a labeled source domain to an unlabeled target domain, existing
methods focus on the distribution alignment between two domains. However, how
to better align source and target features is not well addressed. In this
paper, we propose a deep feature registration (DFR) model to generate
registered features that maintain domain invariant features and simultaneously
minimize the domain-dissimilarity of registered features and target features
via histogram matching. We further employ a pseudo label refinement process,
which considers both probabilistic soft selection and center-based hard
selection to improve the quality of pseudo labels in the target domain.
Extensive experiments on multiple UDA benchmarks demonstrate the effectiveness
of our DFR model, resulting in new state-of-the-art performance.
