  This work addresses the unsupervised domain adaptation problem, especially in
the case of class labels in the target domain being only a subset of those in
the source domain. Such a partial transfer setting is realistic but challenging
and existing methods always suffer from two key problems, negative transfer and
uncertainty propagation. In this paper, we build on domain adversarial learning
and propose a novel domain adaptation method BA$^3$US with two new techniques
termed Balanced Adversarial Alignment (BAA) and Adaptive Uncertainty
Suppression (AUS), respectively. On one hand, negative transfer results in
misclassification of target samples to the classes only present in the source
domain. To address this issue, BAA pursues the balance between label
distributions across domains in a fairly simple manner. Specifically, it
randomly leverages a few source samples to augment the smaller target domain
during domain alignment so that classes in different domains are symmetric. On
the other hand, a source sample would be denoted as uncertain if there is an
incorrect class that has a relatively high prediction score, and such
uncertainty easily propagates to unlabeled target data around it during
alignment, which severely deteriorates adaptation performance. Thus we present
AUS that emphasizes uncertain samples and exploits an adaptive weighted
complement entropy objective to encourage incorrect classes to have uniform and
low prediction scores. Experimental results on multiple benchmarks demonstrate
our BA$^3$US surpasses state-of-the-arts for partial domain adaptation tasks.
Code is available at \url{https://github.com/tim-learn/BA3US}.
