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While\napproaches based on convolutional neural networks constantly break new records\non different benchmarks, generalizing well to diverse testing environments\nremains a major challenge. In numerous real world applications, there is indeed\na large gap between data distributions in train and test domains, which results\nin severe performance loss at run-time. In this work, we address the task of\nunsupervised domain adaptation in semantic segmentation with losses based on\nthe entropy of the pixel-wise predictions. To this end, we propose two novel,\ncomplementary methods using (i) entropy loss and (ii) adversarial loss\nrespectively. We demonstrate state-of-the-art performance in semantic\nsegmentation on two challenging \"synthetic-2-real\" set-ups and show that the\napproach can also be used for detection.","url_abs":"http://arxiv.org/abs/1811.12833v2","url_pdf":"http://arxiv.org/pdf/1811.12833v2.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":"advent-adversarial-entropy-minimization-for","repo_url":"https://github.com/valeoai/ADVENT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"advent-adversarial-entropy-minimization-for","repo_url":"https://github.com/attm/tensorflow_advent","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"advent-adversarial-entropy-minimization-for","repo_url":"https://github.com/thuml/Transfer-Learning-Library","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"advent-adversarial-entropy-minimization-for","repo_url":"https://github.com/yuan-zm/dgt-st","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"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/domain-adaptation-on-panoptic-synthia-to","task":"Domain Adaptation","dataset":"Panoptic SYNTHIA-to-Cityscapes","model":"ADVENT","rank_in_archive_order":5,"of":5,"metrics":{"mPQ":"28.1"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-panoptic-synthia-to-1","task":"Domain Adaptation","dataset":"Panoptic SYNTHIA-to-Mapillary","model":"ADVENT","rank_in_archive_order":5,"of":5,"metrics":{"mPQ":"18.3"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-synthia-to-cityscapes","task":"Domain Adaptation","dataset":"SYNTHIA-to-Cityscapes","model":"ADVENT (ResNet-101)","rank_in_archive_order":27,"of":33,"metrics":{"mIoU":"41.2"},"uses_additional_data":false},{"leaderboard":"/sota/image-to-image-translation-on-gtav-to","task":"Image-to-Image Translation","dataset":"GTAV-to-Cityscapes Labels","model":"ADVENT","rank_in_archive_order":19,"of":22,"metrics":{"mIoU":"44.8"},"uses_additional_data":false},{"leaderboard":"/sota/image-to-image-translation-on-synthia-to","task":"Image-to-Image Translation","dataset":"SYNTHIA-to-Cityscapes","model":"ADVENT","rank_in_archive_order":20,"of":28,"metrics":{"mIoU (13 classes)":"48"},"uses_additional_data":false},{"leaderboard":"/sota/synthetic-to-real-translation-on-gtav-to","task":"Synthetic-to-Real Translation","dataset":"GTAV-to-Cityscapes Labels","model":"AdvEnt(with MinEnt)","rank_in_archive_order":59,"of":73,"metrics":{"mIoU":"45.5"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1811.12833","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.12833"}},"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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