{"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/return-of-frustratingly-easy-domain","title":"Return of Frustratingly Easy Domain Adaptation","arxiv_id":"1511.05547","date":"2015-11-17","proceeding":null,"authors":["Baochen Sun","Jiashi Feng","Kate Saenko"],"abstract":"Unlike human learning, machine learning often fails to handle changes between\ntraining (source) and test (target) input distributions. Such domain shifts,\ncommon in practical scenarios, severely damage the performance of conventional\nmachine learning methods. Supervised domain adaptation methods have been\nproposed for the case when the target data have labels, including some that\nperform very well despite being \"frustratingly easy\" to implement. However, in\npractice, the target domain is often unlabeled, requiring unsupervised\nadaptation. We propose a simple, effective, and efficient method for\nunsupervised domain adaptation called CORrelation ALignment (CORAL). CORAL\nminimizes domain shift by aligning the second-order statistics of source and\ntarget distributions, without requiring any target labels. Even though it is\nextraordinarily simple--it can be implemented in four lines of Matlab\ncode--CORAL performs remarkably well in extensive evaluations on standard\nbenchmark datasets.","url_abs":"http://arxiv.org/abs/1511.05547v2","url_pdf":"http://arxiv.org/pdf/1511.05547v2.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":"return-of-frustratingly-easy-domain","repo_url":"https://github.com/vincent-vercruyssen/transfertools","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-adaptation-on-synth-digits-to-svhn","task":"Domain Adaptation","dataset":"Synth Digits-to-SVHN","model":"CORAL [sun2015return]","rank_in_archive_order":4,"of":4,"metrics":{"Accuracy":"85.2"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-synth-signs-to-gtsrb","task":"Domain Adaptation","dataset":"Synth Signs-to-GTSRB","model":"CORAL [sun2015return]","rank_in_archive_order":4,"of":4,"metrics":{"Accuracy":"86.9"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1511.05547","atlas_url":"https://app.syntology.ai/?focus=1511.05547","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.05547"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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