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.

我们提出了一种简单直接的方法来减小不同领域之间的差异，该方法无需进行额外的参数计算，并且可以与基于自训练的无监督领域适应方法无缝集成。通过在潜在特征空间中将目标领域的风格传递给源领域，该模型在决策过程中优先考虑目标领域的风格，在图像级别和浅层特征图级别上解决了该问题，并实现了在目标领域上出色的性能。我们的方法在合成到真实的无监督领域适应任务中取得了显著的性能提升，比如在 GTA->Cityscapes 数据集上达到了显著的 UDA 性能，mIoU 为 76.93%，比先前最先进结果改善了 1.03 个百分点。