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Unsupervised Domain Adaptation for Semantic Segmentation by Content Transfer
[article]
2020
arXiv
pre-print
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,
arXiv:2012.12545v1
fatcat:qxdokvfw65ekto4cydmindcn2m