  Extensive studies on Unsupervised Domain Adaptation (UDA) have propelled the
deployment of deep learning from limited experimental datasets into real-world
unconstrained domains. Most UDA approaches align features within a common
embedding space and apply a shared classifier for target prediction. However,
since a perfectly aligned feature space may not exist when the domain
discrepancy is large, these methods suffer from two limitations. First, the
coercive domain alignment deteriorates target domain discriminability due to
lacking target label supervision. Second, the source-supervised classifier is
inevitably biased to source data, thus it may underperform in target domain. To
alleviate these issues, we propose to simultaneously conduct feature alignment
in two individual spaces focusing on different domains, and create for each
space a domain-oriented classifier tailored specifically for that domain.
Specifically, we design a Domain-Oriented Transformer (DOT) that has two
individual classification tokens to learn different domain-oriented
representations, and two classifiers to preserve domain-wise discriminability.
Theoretical guaranteed contrastive-based alignment and the source-guided
pseudo-label refinement strategy are utilized to explore both domain-invariant
and specific information. Comprehensive experiments validate that our method
achieves state-of-the-art on several benchmarks.
