Semantic Consistency in Image-to-Image Translation for Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2111.03522v1
• Date: Fri, 5 Nov 2021 14:22:20 GMT
• Title: Semantic Consistency in Image-to-Image Translation for Unsupervised Domain Adaptation
• Authors: Stephan Brehm and Sebastian Scherer and Rainer Lienhart
• Abstract summary: Unsupervised Domain Adaptation (UDA) aims to adapt models trained on a source domain to a new target domain where no labelled data is available. We propose a semantically consistent image-to-image translation method in combination with a consistency regularisation method for UDA.
• Score: 22.269565708490465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 adaption benchmarks, i.e., on GTA5 to Cityscapes and SYNTHIA to Cityscapes.

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