Rethinking Unsupervised Domain Adaptation for Semantic Segmentation. (arXiv:2207.00067v2 [cs.CV] UPDATED)

Unsupervised domain adaptation (UDA) adapts a model trained on one domain
(called source) to a novel domain (called target) using only unlabeled data.
Due to its high annotation cost, researchers have developed many UDA methods
for semantic segmentation, which assume no labeled sample is available in the
target domain. We question the practicality of this assumption for two reasons.
First, after training a model with a UDA method, we must somehow verify the
model before deployment. Second, UDA methods have …

arxiv domain adaptation segmentation semantic unsupervised