  Unsupervised domain adaptation (UDA) deals with the problem of classifying
unlabeled target domain data while labeled data is only available for a
different source domain. Unfortunately, commonly used classification methods
cannot fulfill this task adequately due to the domain gap between the source
and target data. In this paper, we propose a novel uncertainty-aware domain
adaptation setup that models uncertainty as a multivariate Gaussian
distribution in feature space. We show that our proposed uncertainty measure
correlates with other common uncertainty quantifications and relates to
smoothing the classifier's decision boundary, therefore improving the
generalization capabilities. We evaluate our proposed pipeline on challenging
UDA datasets and achieve state-of-the-art results. Code for our method is
available at https://gitlab.com/tringwald/cvp.
