  Unsupervised Domain Adaptation (UDA) refers to the method that utilizes
annotated source domain data and unlabeled target domain data to train a model
capable of generalizing to the target domain data. Domain discrepancy leads to
a significant decrease in the performance of general network models trained on
the source domain data when applied to the target domain. We introduce a
straightforward approach to mitigate the domain discrepancy, which necessitates
no additional parameter calculations and seamlessly integrates with
self-training-based UDA methods. Through the transfer of the target domain
style to the source domain in the latent feature space, the model is trained to
prioritize the target domain style during the decision-making process. We
tackle the problem at both the image-level and shallow feature map level by
transferring the style information from the target domain to the source domain
data. As a result, we obtain a model that exhibits superior performance on the
target domain. Our method yields remarkable enhancements in the
state-of-the-art performance for synthetic-to-real UDA tasks. For example, our
proposed method attains a noteworthy UDA performance of 76.93 mIoU on the
GTA->Cityscapes dataset, representing a notable improvement of +1.03 percentage
points over the previous state-of-the-art results.
