  Unsupervised domain adaptation in semantic segmentation has been raised to
alleviate the reliance on expensive pixel-wise annotations. It leverages a
labeled source domain dataset as well as unlabeled target domain images to
learn a segmentation network. In this paper, we observe two main issues of the
existing domain-invariant learning framework. (1) Being distracted by the
feature distribution alignment, the network cannot focus on the segmentation
task. (2) Fitting source domain data well would compromise the target domain
performance. To address these issues, we propose DecoupleNet that alleviates
source domain overfitting and enables the final model to focus more on the
segmentation task. Furthermore, we put forward Self-Discrimination (SD) and
introduce an auxiliary classifier to learn more discriminative target domain
features with pseudo labels. Finally, we propose Online Enhanced Self-Training
(OEST) to contextually enhance the quality of pseudo labels in an online
manner. Experiments show our method outperforms existing state-of-the-art
methods, and extensive ablation studies verify the effectiveness of each
component. Code is available at https://github.com/dvlab-research/DecoupleNet.
