  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from
a labeled source domain to a different unlabeled target domain. Most existing
UDA methods focus on learning domain-invariant feature representation, either
from the domain level or category level, using convolution neural networks
(CNNs)-based frameworks. One fundamental problem for the category level based
UDA is the production of pseudo labels for samples in target domain, which are
usually too noisy for accurate domain alignment, inevitably compromising the
UDA performance. With the success of Transformer in various tasks, we find that
the cross-attention in Transformer is robust to the noisy input pairs for
better feature alignment, thus in this paper Transformer is adopted for the
challenging UDA task. Specifically, to generate accurate input pairs, we design
a two-way center-aware labeling algorithm to produce pseudo labels for target
samples. Along with the pseudo labels, a weight-sharing triple-branch
transformer framework is proposed to apply self-attention and cross-attention
for source/target feature learning and source-target domain alignment,
respectively. Such design explicitly enforces the framework to learn
discriminative domain-specific and domain-invariant representations
simultaneously. The proposed method is dubbed CDTrans (cross-domain
transformer), and it provides one of the first attempts to solve UDA tasks with
a pure transformer solution. Experiments show that our proposed method achieves
the best performance on public UDA datasets, e.g. VisDA-2017 and DomainNet.
Code and models are available at https://github.com/CDTrans/CDTrans.
