Semantically Consistent Image-to-Image Translation for Unsupervised Domain Adaptation
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Abstract (arXiv)
Unsupervised Domain Adaptation (UDA) aims to adapt models trained on a source domain to a new target domain where no labelled data is available. In this work, we investigate the problem of UDA from a synthetic computer-generated domain to a similar but real-world domain for learning semantic segmentation. We propose a semantically consistent image-to-image translation method in combination with a consistency regularisation method for UDA. We overcome previous limitations on transferring synthetic images to real looking images. We leverage pseudo-labels in order to learn a generative image-to-image translation model that receives additional feedback from semantic labels on both domains. Our method outperforms state-of-the-art methods that combine image-to-image translation and semi-supervised learning on relevant domain adaptation benchmarks, i.e., on GTA5 to Cityscapes and SYNTHIA to Cityscapes.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 32 of its 46 references that are in the map. First posted on arXiv 5 Nov 2021.
Builds on
32 of 46 references.
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20 more
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2 papers.