{"id":13717419,"url":"https://github.com/thuml/Xlearn","last_synced_at":"2025-05-07T07:31:32.408Z","repository":{"id":72794929,"uuid":"104735756","full_name":"thuml/Xlearn","owner":"thuml","description":"Transfer Learning Library","archived":false,"fork":false,"pushed_at":"2021-04-09T12:31:53.000Z","size":9081,"stargazers_count":459,"open_issues_count":21,"forks_count":155,"subscribers_count":16,"default_branch":"master","last_synced_at":"2024-08-04T00:13:21.434Z","etag":null,"topics":["deep-learning","transfer-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/thuml.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2017-09-25T10:23:31.000Z","updated_at":"2024-06-03T02:38:21.000Z","dependencies_parsed_at":null,"dependency_job_id":"e95164d9-b02c-486f-bee1-98933a816c79","html_url":"https://github.com/thuml/Xlearn","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thuml%2FXlearn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thuml%2FXlearn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thuml%2FXlearn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thuml%2FXlearn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/thuml","download_url":"https://codeload.github.com/thuml/Xlearn/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224573523,"owners_count":17333804,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","transfer-learning"],"created_at":"2024-08-03T00:01:21.935Z","updated_at":"2024-11-14T05:31:42.724Z","avatar_url":"https://github.com/thuml.png","language":"Jupyter Notebook","readme":"# Xlearn (Obsolete, upgraded to https://github.com/thuml/Transfer-Learning-Library)\nTransfer Learning Library\n\nThis is the transfer learning library for the following paper:\n\n### Learning Transferable Features with Deep Adaptation Networks\n### Unsupervised Domain Adaptation with Residual Transfer Networks\n### Deep Transfer Learning with Joint Adaptation Networks\n\nThe tensorflow versions are under developing.\n\n## Citation\nIf you use this code for your research, please consider citing:\n```\n    @inproceedings{DBLP:conf/icml/LongC0J15,\n      author    = {Mingsheng Long and\n                   Yue Cao and\n                   Jianmin Wang and\n                   Michael I. Jordan},\n      title     = {Learning Transferable Features with Deep Adaptation Networks},\n      booktitle = {Proceedings of the 32nd International Conference on Machine Learning,\n                   {ICML} 2015, Lille, France, 6-11 July 2015},\n      pages     = {97--105},\n      year      = {2015},\n      crossref  = {DBLP:conf/icml/2015},\n      url       = {http://jmlr.org/proceedings/papers/v37/long15.html},\n      timestamp = {Tue, 12 Jul 2016 21:51:15 +0200},\n      biburl    = {http://dblp2.uni-trier.de/rec/bib/conf/icml/LongC0J15},\n      bibsource = {dblp computer science bibliography, http://dblp.org}\n    }\n    \n    @inproceedings{DBLP:conf/nips/LongZ0J16,\n      author    = {Mingsheng Long and\n                   Han Zhu and\n                   Jianmin Wang and\n                   Michael I. Jordan},\n      title     = {Unsupervised Domain Adaptation with Residual Transfer Networks},\n      booktitle = {Advances in Neural Information Processing Systems 29: Annual Conference\n                   on Neural Information Processing Systems 2016, December 5-10, 2016,\n                   Barcelona, Spain},\n      pages     = {136--144},\n      year      = {2016},\n      crossref  = {DBLP:conf/nips/2016},\n      url       = {http://papers.nips.cc/paper/6110-unsupervised-domain-adaptation-with-residual-transfer-networks},\n      timestamp = {Fri, 16 Dec 2016 19:45:58 +0100},\n      biburl    = {http://dblp.uni-trier.de/rec/bib/conf/nips/LongZ0J16},\n      bibsource = {dblp computer science bibliography, http://dblp.org}\n    }\n    \n    @inproceedings{DBLP:conf/icml/LongZ0J17,\n      author    = {Mingsheng Long and\n                   Han Zhu and\n                   Jianmin Wang and\n                   Michael I. Jordan},\n      title     = {Deep Transfer Learning with Joint Adaptation Networks},\n      booktitle = {Proceedings of the 34th International Conference on Machine Learning,\n               {ICML} 2017, Sydney, NSW, Australia, 6-11 August 2017},\n      pages     = {2208--2217},\n      year      = {2017},\n      crossref  = {DBLP:conf/icml/2017},\n      url       = {http://proceedings.mlr.press/v70/long17a.html},\n      timestamp = {Tue, 25 Jul 2017 17:27:57 +0200},\n      biburl    = {http://dblp.uni-trier.de/rec/bib/conf/icml/LongZ0J17},\n      bibsource = {dblp computer science bibliography, http://dblp.org}\n    }\n```\n\n## Contact\nIf you have any problem about our code, feel free to contact \n- longmingsheng@gmail.com\n- youkaichao@gmail.com\n\nor describe your problem in Issues.\n","funding_links":[],"categories":["Pytorch \u0026 related libraries｜Pytorch \u0026 相关库","Pytorch \u0026 related libraries","Benchmarks"],"sub_categories":["Other libraries｜其他库:","Other libraries:","Disentangled Representation Learning"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthuml%2FXlearn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthuml%2FXlearn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthuml%2FXlearn/lists"}