  Unsupervised domain adaptation (UDA) aims to improve the prediction
performance in the target domain under distribution shifts from the source
domain. The key principle of UDA is to minimize the divergence between the
source and the target domains. To follow this principle, many methods employ a
domain discriminator to match the feature distributions. Some recent methods
evaluate the discrepancy between two predictions on target samples to detect
those that deviate from the source distribution. However, their performance is
limited because they either match the marginal distributions or measure the
divergence conservatively. In this paper, we present a novel UDA method that
learns domain-invariant features that minimize the domain divergence. We
propose model uncertainty as a measure of the domain divergence. Our UDA method
based on model uncertainty (MUDA) adopts a Bayesian framework and provides an
efficient way to evaluate model uncertainty by means of Monte Carlo dropout
sampling. Empirical results on image recognition tasks show that our method is
superior to existing state-of-the-art methods. We also extend MUDA to
multi-source domain adaptation problems.
