  In this paper we tackle the problem of unsupervised domain adaptation for the
task of semantic segmentation, where we attempt to transfer the knowledge
learned upon synthetic datasets with ground-truth labels to real-world images
without any annotation. With the hypothesis that the structural content of
images is the most informative and decisive factor to semantic segmentation and
can be readily shared across domains, we propose a Domain Invariant Structure
Extraction (DISE) framework to disentangle images into domain-invariant
structure and domain-specific texture representations, which can further
realize image-translation across domains and enable label transfer to improve
segmentation performance. Extensive experiments verify the effectiveness of our
proposed DISE model and demonstrate its superiority over several
state-of-the-art approaches.
