  Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a
labeled source domain to an unlabeled target domain. Most existing UDA
approaches enable knowledge transfer via learning domain-invariant
representation and sharing one classifier across two domains. However, ignoring
the domain-specific information that are related to the task, and forcing a
unified classifier to fit both domains will limit the feature expressiveness in
each domain. In this paper, by observing that the Transformer architecture with
comparable parameters can generate more transferable representations than CNN
counterparts, we propose a Win-Win TRansformer framework (WinTR) that
separately explores the domain-specific knowledge for each domain and meanwhile
interchanges cross-domain knowledge. Specifically, we learn two different
mappings using two individual classification tokens in the Transformer, and
design for each one a domain-specific classifier. The cross-domain knowledge is
transferred via source guided label refinement and single-sided feature
alignment with respect to source or target, which keeps the integrity of
domain-specific information. Extensive experiments on three benchmark datasets
show that our method outperforms the state-of-the-art UDA methods, validating
the effectiveness of exploiting both domain-specific and invariant
