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In this paper, we propose an orthogonal method, called memory regularization in vivo to exploit the intra-domain knowledge and regularize the model training. Specifically, we refer to the segmentation model itself as the memory module, and minor the discrepancy of the two classifiers, i.e., the primary classifier and the auxiliary classifier, to reduce the prediction inconsistency. Without extra parameters, the proposed method is complementary to the most existing domain adaptation methods and could generally improve the performance of existing methods. Albeit simple, we verify the effectiveness of memory regularization on two synthetic-to-real benchmarks: GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, yielding +11.1% and +11.3% mIoU improvement over the baseline model, respectively. Besides, a similar +12.0% mIoU improvement is observed on the cross-city benchmark: Cityscapes -> Oxford RobotCar.","url_abs":"https://arxiv.org/abs/1912.11164v3","url_pdf":"https://arxiv.org/pdf/1912.11164v3.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":"unsupervised-scene-adaptation-with-memory","repo_url":"https://github.com/layumi/Seg-Uncertainty","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"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":[{"method_slug":"memory-network","method_name":"Memory Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-adaptation-on-gta5-to-cityscapes","task":"Domain Adaptation","dataset":"GTA5 to Cityscapes","model":"MRNet","rank_in_archive_order":28,"of":28,"metrics":{"mIoU":"48.3"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-gta5-synscapes-to","task":"Domain Adaptation","dataset":"GTA5+Synscapes to Cityscapes","model":"MRNet","rank_in_archive_order":5,"of":5,"metrics":{"mIoU":"47.6"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-gtav-synscapes-to","task":"Domain Adaptation","dataset":"GTAV+Synscapes to Cityscapes","model":"MRNet","rank_in_archive_order":6,"of":6,"metrics":{"mIoU":"47.6"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-synthia-to-cityscapes-1","task":"Domain Adaptation","dataset":"SYNTHIA-to-Cityscapes Labels","model":"MRNet","rank_in_archive_order":1,"of":1,"metrics":{"mIoU":"46.5"},"uses_additional_data":false},{"leaderboard":"/sota/synthetic-to-real-translation-on-gtav-to","task":"Synthetic-to-Real Translation","dataset":"GTAV-to-Cityscapes Labels","model":"MRNet","rank_in_archive_order":53,"of":73,"metrics":{"mIoU":"48.3"},"uses_additional_data":false},{"leaderboard":"/sota/synthetic-to-real-translation-on-synthia-to-1","task":"Synthetic-to-Real Translation","dataset":"SYNTHIA-to-Cityscapes","model":"MRNet(ResNet-101)","rank_in_archive_order":36,"of":38,"metrics":{"MIoU (13 classes)":"53.8"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-cityscapes-2","task":"Unsupervised Domain Adaptation","dataset":"Cityscapes-to-OxfordCar","model":"MRNet","rank_in_archive_order":3,"of":4,"metrics":{"mIoU":"73.9"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-gtav-to","task":"Unsupervised Domain Adaptation","dataset":"GTAV-to-Cityscapes Labels","model":"MRNet","rank_in_archive_order":20,"of":20,"metrics":{"mIoU":"45.5"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-synthia-to","task":"Unsupervised Domain Adaptation","dataset":"SYNTHIA-to-Cityscapes","model":"MRNet","rank_in_archive_order":21,"of":23,"metrics":{"mIoU":"43.2","mIoU (13 classes)":"50.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1912.11164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.11164"}},"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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