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Existing domain adaptation techniques either work on limited\ndatasets, or yield not so good performance compared with supervised learning.\nIn this paper, we propose a novel bidirectional learning framework for domain\nadaptation of segmentation. Using the bidirectional learning, the image\ntranslation model and the segmentation adaptation model can be learned\nalternatively and promote to each other. Furthermore, we propose a\nself-supervised learning algorithm to learn a better segmentation adaptation\nmodel and in return improve the image translation model. Experiments show that\nour method is superior to the state-of-the-art methods in domain adaptation of\nsegmentation with a big margin. The source code is available at\nhttps://github.com/liyunsheng13/BDL.","url_abs":"http://arxiv.org/abs/1904.10620v1","url_pdf":"http://arxiv.org/pdf/1904.10620v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bidirectional-learning-for-domain-adaptation","repo_url":"https://github.com/liyunsheng13/BDL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"bidirectional-learning-for-domain-adaptation","repo_url":"https://github.com/Sudhir11292rt/myBDL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"bidirectional-learning-for-domain-adaptation","repo_url":"https://github.com/asbjrnmunk/mdd-unet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"synthetic-to-real-translation","task_name":"Synthetic-to-Real Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-to-image-translation-on-gtav-to","task":"Image-to-Image Translation","dataset":"GTAV-to-Cityscapes Labels","model":"Bidirectional Learning","rank_in_archive_order":21,"of":22,"metrics":{"mIoU":"41.3"},"uses_additional_data":false},{"leaderboard":"/sota/image-to-image-translation-on-synthia-to","task":"Image-to-Image Translation","dataset":"SYNTHIA-to-Cityscapes","model":"Bidirectional Learning (ResNet-101)","rank_in_archive_order":16,"of":28,"metrics":{"mIoU (13 classes)":"51.4"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-dada-seg","task":"Semantic Segmentation","dataset":"DADA-seg","model":"BDL","rank_in_archive_order":7,"of":28,"metrics":{"mIoU":"29.66"},"uses_additional_data":false},{"leaderboard":"/sota/synthetic-to-real-translation-on-gtav-to","task":"Synthetic-to-Real Translation","dataset":"GTAV-to-Cityscapes Labels","model":"BDL","rank_in_archive_order":52,"of":73,"metrics":{"mIoU":"48.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.10620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.10620"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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