  Deep learning-based solutions for semantic segmentation suffer from
significant performance degradation when tested on data with different
characteristics than what was used during the training. Adapting the models
using annotated data from the new domain is not always practical. Unsupervised
Domain Adaptation (UDA) approaches are crucial in deploying these models in the
actual operating conditions. Recent state-of-the-art (SOTA) UDA methods employ
a teacher-student self-training approach, where a teacher model is used to
generate pseudo-labels for the new data which in turn guide the training
process of the student model. Though this approach has seen a lot of success,
it suffers from the issue of noisy pseudo-labels being propagated in the
training process. To address this issue, we propose an auxiliary pseudo-label
refinement network (PRN) for online refining of the pseudo labels and also
localizing the pixels whose predicted labels are likely to be noisy. Being able
to improve the quality of pseudo labels and select highly reliable ones, PRN
helps self-training of segmentation models to be robust against pseudo label
noise propagation during different stages of adaptation. We evaluate our
approach on benchmark datasets with three different domain shifts, and our
approach consistently performs significantly better than the previous
state-of-the-art methods.
