{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:37:53Z","timestamp":1777703873960,"version":"3.51.4"},"reference-count":56,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T00:00:00Z","timestamp":1573776000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,12,23]]},"abstract":"<jats:p>\n                    Unsupervised domain adaptation (UDA) aims to build a classifier for the unlabeled target domain by transferring knowledge from a well-labeled source domain. Recently deep domain adaptation methods can not effectively integrate discriminability with transferability of features, and these methods can only reduce, but not remove, the cross-domain discrepancy. To this end, this paper proposes a new domain adaptation method called Joint Category-Level and Discriminative Feature Learning Network (CDN). CDN not only achieves domain adaptation by minimizing category-level distribution discrepancy between domains but also learns discriminative feature representations via maximizing inter-category distance and selecting transferability samples simultaneously. Moreover, we develop a\n                    <jats:italic>Transferability Weighting Module<\/jats:italic>\n                    (TWM), which is based on a constructed classifier, to further strengthen the discriminability of sample\u2019s features. The experimental results demonstrate that CDN can significantly decrease the cross-domain distribution inconsistency and further promote the classification performance.\n                  <\/jats:p>","DOI":"10.3233\/jifs-191136","type":"journal-article","created":{"date-parts":[[2019,11,19]],"date-time":"2019-11-19T11:54:20Z","timestamp":1574164460000},"page":"8499-8510","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Joint category-level and discriminative feature learning networks for unsupervised domain adaptation"],"prefix":"10.1177","volume":"37","author":[{"given":"Pengyu","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, South China University of Technology, GuangZhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junchu","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electronic and 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