  In this paper, we tackle the unsupervised domain adaptation (UDA) for
semantic segmentation, which aims to segment the unlabeled real data using
labeled synthetic data. The main problem of UDA for semantic segmentation
relies on reducing the domain gap between the real image and synthetic image.
To solve this problem, we focused on separating information in an image into
content and style. Here, only the content has cues for semantic segmentation,
and the style makes the domain gap. Thus, precise separation of content and
style in an image leads to effect as supervision of real data even when
learning with synthetic data. To make the best of this effect, we propose a
zero-style loss. Even though we perfectly extract content for semantic
segmentation in the real domain, another main challenge, the class imbalance
problem, still exists in UDA for semantic segmentation. We address this problem
by transferring the contents of tail classes from synthetic to real domain.
Experimental results show that the proposed method achieves the
state-of-the-art performance in semantic segmentation on the major two UDA
settings.
