{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-source-domain-adaptation-for-semantic","title":"Multi-source Domain Adaptation for Semantic Segmentation","arxiv_id":"1910.12181","date":"2019-10-27","proceeding":"NeurIPS 2019 12","authors":["Sicheng Zhao","Bo Li","Xiangyu Yue","Yang Gu","Pengfei Xu","Runbo Hu","Hua Chai","Kurt Keutzer"],"abstract":"Simulation-to-real domain adaptation for semantic segmentation has been actively studied for various applications such as autonomous driving. Existing methods mainly focus on a single-source setting, which cannot easily handle a more practical scenario of multiple sources with different distributions. In this paper, we propose to investigate multi-source domain adaptation for semantic segmentation. Specifically, we design a novel framework, termed Multi-source Adversarial Domain Aggregation Network (MADAN), which can be trained in an end-to-end manner. First, we generate an adapted domain for each source with dynamic semantic consistency while aligning at the pixel-level cycle-consistently towards the target. Second, we propose sub-domain aggregation discriminator and cross-domain cycle discriminator to make different adapted domains more closely aggregated. Finally, feature-level alignment is performed between the aggregated domain and target domain while training the segmentation network. Extensive experiments from synthetic GTA and SYNTHIA to real Cityscapes and BDDS datasets demonstrate that the proposed MADAN model outperforms state-of-the-art approaches. Our source code is released at: https://github.com/Luodian/MADAN.","url_abs":"https://arxiv.org/abs/1910.12181v1","url_pdf":"https://arxiv.org/pdf/1910.12181v1.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":"multi-source-domain-adaptation-for-semantic","repo_url":"https://github.com/Luodian/MADAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-adaptation-on-gta5-synscapes-to","task":"Domain Adaptation","dataset":"GTA5+Synscapes to Cityscapes","model":"MADAN","rank_in_archive_order":2,"of":5,"metrics":{"mIoU":"55.7"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-gtav-synscapes-to","task":"Domain Adaptation","dataset":"GTAV+Synscapes to Cityscapes","model":"MADAN","rank_in_archive_order":3,"of":6,"metrics":{"mIoU":"55.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1910.12181","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.12181"}},"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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