  Unsupervised domain adaptation (UDA) deals with the adaptation of models from
a given source domain with labeled data to an unlabeled target domain. In this
paper, we utilize the inherent prediction uncertainty of a model to accomplish
the domain adaptation task. The uncertainty is measured by Monte-Carlo dropout
and used for our proposed Uncertainty-based Filtering and Feature Alignment
(UFAL) that combines an Uncertain Feature Loss (UFL) function and an
Uncertainty-Based Filtering (UBF) approach for alignment of features in
Euclidean space. Our method surpasses recently proposed architectures and
achieves state-of-the-art results on multiple challenging datasets. Code is
available on the project website.
