{"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/learning-to-transfer-examples-for-partial","title":"Learning to Transfer Examples for Partial Domain Adaptation","arxiv_id":"1903.12230","date":"2019-03-28","proceeding":"CVPR 2019 6","authors":["Zhangjie Cao","Kaichao You","Mingsheng Long","Jian-Min Wang","Qiang Yang"],"abstract":"Domain adaptation is critical for learning in new and unseen environments.\nWith domain adversarial training, deep networks can learn disentangled and\ntransferable features that effectively diminish the dataset shift between the\nsource and target domains for knowledge transfer. In the era of Big Data, the\nready availability of large-scale labeled datasets has stimulated wide interest\nin partial domain adaptation (PDA), which transfers a recognizer from a labeled\nlarge domain to an unlabeled small domain. It extends standard domain\nadaptation to the scenario where target labels are only a subset of source\nlabels. Under the condition that target labels are unknown, the key challenge\nof PDA is how to transfer relevant examples in the shared classes to promote\npositive transfer, and ignore irrelevant ones in the specific classes to\nmitigate negative transfer. In this work, we propose a unified approach to PDA,\nExample Transfer Network (ETN), which jointly learns domain-invariant\nrepresentations across the source and target domains, and a progressive\nweighting scheme that quantifies the transferability of source examples while\ncontrolling their importance to the learning task in the target domain. A\nthorough evaluation on several benchmark datasets shows that our approach\nachieves state-of-the-art results for partial domain adaptation tasks.","url_abs":"http://arxiv.org/abs/1903.12230v2","url_pdf":"http://arxiv.org/pdf/1903.12230v2.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":"learning-to-transfer-examples-for-partial","repo_url":"https://github.com/thuml/ETN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"partial-domain-adaptation","task_name":"Partial Domain Adaptation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/partial-domain-adaptation-on-imagenet-caltech","task":"Partial Domain Adaptation","dataset":"ImageNet-Caltech","model":"ETN","rank_in_archive_order":4,"of":4,"metrics":{"Accuracy (%)":"79.1"},"uses_additional_data":false},{"leaderboard":"/sota/partial-domain-adaptation-on-office-31","task":"Partial Domain Adaptation","dataset":"Office-31","model":"ETN","rank_in_archive_order":7,"of":7,"metrics":{"Accuracy (%)":"96.7"},"uses_additional_data":false},{"leaderboard":"/sota/partial-domain-adaptation-on-office-home","task":"Partial Domain Adaptation","dataset":"Office-Home","model":"ETN","rank_in_archive_order":11,"of":11,"metrics":{"Accuracy (%)":"70.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.12230","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}