{"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/domain-separation-networks","title":"Domain Separation Networks","arxiv_id":"1608.06019","date":"2016-08-22","proceeding":"NeurIPS 2016 12","authors":["Konstantinos Bousmalis","George Trigeorgis","Nathan Silberman","Dilip Krishnan","Dumitru Erhan"],"abstract":"The cost of large scale data collection and annotation often makes the\napplication of machine learning algorithms to new tasks or datasets\nprohibitively expensive. One approach circumventing this cost is training\nmodels on synthetic data where annotations are provided automatically. Despite\ntheir appeal, such models often fail to generalize from synthetic to real\nimages, necessitating domain adaptation algorithms to manipulate these models\nbefore they can be successfully applied. Existing approaches focus either on\nmapping representations from one domain to the other, or on learning to extract\nfeatures that are invariant to the domain from which they were extracted.\nHowever, by focusing only on creating a mapping or shared representation\nbetween the two domains, they ignore the individual characteristics of each\ndomain. We suggest that explicitly modeling what is unique to each domain can\nimprove a model's ability to extract domain-invariant features. Inspired by\nwork on private-shared component analysis, we explicitly learn to extract image\nrepresentations that are partitioned into two subspaces: one component which is\nprivate to each domain and one which is shared across domains. Our model is\ntrained not only to perform the task we care about in the source domain, but\nalso to use the partitioned representation to reconstruct the images from both\ndomains. Our novel architecture results in a model that outperforms the\nstate-of-the-art on a range of unsupervised domain adaptation scenarios and\nadditionally produces visualizations of the private and shared representations\nenabling interpretation of the domain adaptation process.","url_abs":"http://arxiv.org/abs/1608.06019v1","url_pdf":"http://arxiv.org/pdf/1608.06019v1.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":"domain-separation-networks","repo_url":"https://github.com/tensorflow/models","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"domain-separation-networks","repo_url":"https://github.com/AmirHussein96/Simplified-DSN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"domain-separation-networks","repo_url":"https://github.com/WinChua/CDRTR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-2-Clause"}},{"paper_slug":"domain-separation-networks","repo_url":"https://github.com/better-chao/DSN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"domain-separation-networks","repo_url":"https://github.com/fungtion/DSN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"domain-separation-networks","repo_url":"https://github.com/tensorflow/models/tree/master/research/domain_adaptation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-adaptation-on-mnist-to-mnist-m","task":"Domain Adaptation","dataset":"MNIST-to-MNIST-M","model":"DSN (DANN)","rank_in_archive_order":3,"of":5,"metrics":{"Accuracy":"83.2"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-svnh-to-mnist","task":"Domain Adaptation","dataset":"SVNH-to-MNIST","model":"DSN (DANN)","rank_in_archive_order":7,"of":9,"metrics":{"Accuracy":"82.7"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-synth-digits-to-svhn","task":"Domain Adaptation","dataset":"Synth Digits-to-SVHN","model":"DSN (DANN)","rank_in_archive_order":1,"of":4,"metrics":{"Accuracy":"91.2"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-synth-objects-to-linemod","task":"Domain Adaptation","dataset":"Synth Objects-to-LINEMOD","model":"DSN (DANN)","rank_in_archive_order":1,"of":1,"metrics":{"Classification Accuracy":"100","Mean Angle Error":"53.27"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-synth-signs-to-gtsrb","task":"Domain Adaptation","dataset":"Synth Signs-to-GTSRB","model":"DSN (DANN)","rank_in_archive_order":2,"of":4,"metrics":{"Accuracy":"93.1"},"uses_additional_data":false},{"leaderboard":"/sota/domain-generalization-on-pacs-2","task":"Domain Generalization","dataset":"PACS","model":"DSN (Alexnet)","rank_in_archive_order":129,"of":133,"metrics":{"Average Accuracy":"67.37"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1608.06019","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1608.06019"}},"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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