  Unsupervised domain adaptation (UDA) deals with the adaptation process of a
model to an unlabeled target domain while annotated data is only available for
a given source domain. This poses a challenging task, as the domain shift
between source and target instances deteriorates a model's performance when not
addressed. In this paper, we propose UBR$^2$S - the Uncertainty-Based
Resampling and Reweighting Strategy - to tackle this problem. UBR$^2$S employs
a Monte Carlo dropout-based uncertainty estimate to obtain per-class
probability distributions, which are then used for dynamic resampling of
pseudo-labels and reweighting based on their sample likelihood and the
accompanying decision error. Our proposed method achieves state-of-the-art
results on multiple UDA datasets with single and multi-source adaptation tasks
and can be applied to any off-the-shelf network architecture. Code for our
method is available at https://gitlab.com/tringwald/UBR2S.
