{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T02:40:04Z","timestamp":1783392004296,"version":"3.54.6"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,2,16]],"date-time":"2023-02-16T00:00:00Z","timestamp":1676505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62176128"],"award-info":[{"award-number":["62176128"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Open Projects Program of State Key Laboratory for Novel Software Technology of Nanjing University","award":["KFKT2022B06"],"award-info":[{"award-number":["KFKT2022B06"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["NJ2022028"],"award-info":[{"award-number":["NJ2022028"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2023,4,30]]},"abstract":"<jats:p>\n            <jats:bold>Source-free unsupervised domain adaptation (SFUDA)<\/jats:bold>\n            aims to accomplish the task of adaptation to the target domain by utilizing pre-trained source domain model and unlabeled target domain samples, without directly accessing any source domain data. Although many SFUDA works use the pseudo-labeling strategy to improve the accuracy of pseudo-labels in the target domain, these strategies ignore the influence of domain shift on calculating the reference distribution of pseudo-labels. In this article, we propose a novel kind of SFUDA with trusted pseudo samples (SFUDA-TPS), which uses reliable feature reference distribution to solve the SFUDA problem. In SFUDA-TPS, we design a target feature correcting classifier to alleviate the problem of feature reference distribution deviating from target domain samples distribution. On this basis, the more reliable feature reference distribution is calculated by selecting the target domain samples with a high amount of information, i.e., low entropy in the fixed source domain classifier and target feature correcting classifier. The implicit alignment between the source domain and target domain is realized by learning the source domain distributions hidden in the fixed source domain classifier. Experimental evaluations illustrate the effectiveness of our proposed method in solving SFUDA tasks.\n          <\/jats:p>","DOI":"10.1145\/3570510","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T11:28:39Z","timestamp":1667474919000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Source-free Unsupervised Domain Adaptation with Trusted Pseudo Samples"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0030-3645","authenticated-orcid":false,"given":"Qing","family":"Tian","sequence":"first","affiliation":[{"name":"Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6813-2286","authenticated-orcid":false,"given":"Shun","family":"Peng","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2320-1692","authenticated-orcid":false,"given":"Tinghuai","family":"Ma","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,16]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"e_1_3_2_3_2","article-title":"Self-supervised noisy label learning for source-free unsupervised domain adaptation","author":"Chen Weijie","year":"2021","unstructured":"Weijie Chen, Luojun Lin, Shicai Yang, Di Xie, Shiliang Pu, Yueting Zhuang, and Wenqi Ren. 2021. 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