Abstract
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.
Abstract (translated)
无监督领域适应（UDA）是指利用已标注的源域数据和未标注的目标域数据来训练一个能够泛化到目标域数据的模型。领域差异导致在将基于源域数据的通用网络模型应用于目标域数据时，模型的性能显著下降。我们引入了一种直接的方法来减轻领域差异，这不需要额外的参数计算，并无缝地与基于自训练的UDA方法集成。通过将目标域的风格信息传递到源域的潜在特征空间中，模型在决策过程中优先考虑目标域的风格。我们通过从目标域数据中传递样式信息来解决该问题。结果，我们在目标域上获得了卓越的性能。我们的方法在合成-真实UDA任务上的最先进性能有了显著提高。例如，与之前的结果相比，我们提出的UDA性能达到了+1.03%的显著提高。
URL
https://arxiv.org/abs/2404.16301
