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A\npopular strategy is to align the marginal distribution in the feature space\nthrough adversarial learning. However, this global alignment strategy does not\nconsider the local category-level feature distribution. A possible consequence\nof the global movement is that some categories which are originally well\naligned between the source and target may be incorrectly mapped. To address\nthis problem, this paper introduces a category-level adversarial network,\naiming to enforce local semantic consistency during the trend of global\nalignment. Our idea is to take a close look at the category-level data\ndistribution and align each class with an adaptive adversarial loss.\nSpecifically, we reduce the weight of the adversarial loss for category-level\naligned features while increasing the adversarial force for those poorly\naligned. In this process, we decide how well a feature is category-level\naligned between source and target by a co-training approach. In two domain\nadaptation tasks, i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, we\nvalidate that the proposed method matches the state of the art in segmentation\naccuracy.","url_abs":"http://arxiv.org/abs/1809.09478v3","url_pdf":"http://arxiv.org/pdf/1809.09478v3.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":"taking-a-closer-look-at-domain-shift-category","repo_url":"https://github.com/RoyalVane/CLAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"synthetic-to-real-translation","task_name":"Synthetic-to-Real Translation"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-dada-seg","task":"Semantic Segmentation","dataset":"DADA-seg","model":"CLAN","rank_in_archive_order":8,"of":28,"metrics":{"mIoU":"28.76"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-densepass","task":"Semantic Segmentation","dataset":"DensePASS","model":"CLAN","rank_in_archive_order":25,"of":36,"metrics":{"mIoU":"31.46%"},"uses_additional_data":false},{"leaderboard":"/sota/synthetic-to-real-translation-on-gtav-to","task":"Synthetic-to-Real Translation","dataset":"GTAV-to-Cityscapes Labels","model":"CLAN","rank_in_archive_order":63,"of":73,"metrics":{"mIoU":"43.2"},"uses_additional_data":false},{"leaderboard":"/sota/synthetic-to-real-translation-on-synthia-to-1","task":"Synthetic-to-Real Translation","dataset":"SYNTHIA-to-Cityscapes","model":"CLAN","rank_in_archive_order":37,"of":38,"metrics":{"MIoU (13 classes)":"47.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.09478","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.09478"}},"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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