  Recent works on unsupervised domain adaptation (UDA) focus on the selection
of good pseudo-labels as surrogates for the missing labels in the target data.
However, source domain bias that deteriorates the pseudo-labels can still exist
since the shared network of the source and target domains are typically used
for the pseudo-label selections. The suboptimal feature space source-to-target
domain alignment can also result in unsatisfactory performance. In this paper,
we propose CA-UDA to improve the quality of the pseudo-labels and UDA results
with optimal assignment, a pseudo-label refinement strategy and class-aware
domain alignment. We use an auxiliary network to mitigate the source domain
bias for pseudo-label refinement. Our intuition is that the underlying
semantics in the target domain can be fully exploited to help refine the
pseudo-labels that are inferred from the source features under domain shift.
Furthermore, our optimal assignment can optimally align features in the
source-to-target domains and our class-aware domain alignment can
simultaneously close the domain gap while preserving the classification
decision boundaries. Extensive experiments on several benchmark datasets show
that our method can achieve state-of-the-art performance in the image
classification task.
