{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:15:27Z","timestamp":1770272127061,"version":"3.49.0"},"reference-count":19,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"DOI":"10.13039\/501100001868","name":"National Science Council of Taiwan","doi-asserted-by":"crossref","award":["MOST 110-2221-E-032-031"],"award-info":[{"award-number":["MOST 110-2221-E-032-031"]}],"id":[{"id":"10.13039\/501100001868","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,3,15]]},"abstract":"<jats:p> Unsupervised domain adaptation (UDA) mainly explores how to learn domain-invariant features from the source domain when the target domain label is unknown. To learn domain-invariant features requires aligning the distribution of samples from two domains in the feature space, which can be achieved by minimizing the maximum mean discrepancy (MMD) of samples from the two domains. However, there is still no effective way to find the best parameter values of MMD. Such a problem is addressed in the MMD with deep kernels (MMD-D), whose optimal parameters can be obtained through training. This study proposes a method of domain-invariant feature learning for UDA, whose architecture, named MMDDCDA, comprises an MMD-D module and a cross domain adaptation (CDA) module. MMDDCDA performs alternating training similar to adversarial training to alternately boost the power of the two modules. To our knowledge, this is the first UDA method that performs such alternating training on a UDA architecture using MMD with deep kernels. Experimental validation showed that the proposed method yields state-of-the-art results among UDA methods using other MMD variants and some UDA benchmarks. <\/jats:p>","DOI":"10.1142\/s0218001423510023","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T05:31:47Z","timestamp":1674192707000},"source":"Crossref","is-referenced-by-count":3,"title":["Domain-Invariant Feature Learning for Domain Adaptation"],"prefix":"10.1142","volume":"37","author":[{"given":"Ching-Ting","family":"Tu","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, National Chung Hsing University, Taichung 401, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hsiau-Wen","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Information Management, Chihlee University of Technology, Banqiao Dist., New Taipei City 220305, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1539-233X","authenticated-orcid":false,"given":"Hwei Jen","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Tamkang University, Tamsui Dist., New Taipei City 251, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshimasa","family":"Tokuyama","sequence":"additional","affiliation":[{"name":"Department of Media and Image Technology, Faculty of Engineering, Tokyo Polytechnic University, Tokyo 164-8678, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chia-Hung","family":"Chu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Tamkang University, Tamsui Dist., New Taipei City 251, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"key":"S0218001423510023BIB001","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1093\/bioinformatics\/btl242","volume":"22","author":"Borgwardt K. 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Discov."},{"key":"S0218001423510023BIB019","first-page":"2208","volume-title":"Proc. Int. Conf. Machine Learning (ICML)","author":"Long M."},{"key":"S0218001423510023BIB020","first-page":"97","volume-title":"Proc. 32nd Int. Conf. Machine Learning (PMLR)","author":"Long M."},{"key":"S0218001423510023BIB021","first-page":"1","volume-title":"NIPS Workshop on Deep Learning and Unsupervised Feature Learning","author":"Netzer Y."},{"key":"S0218001423510023BIB022","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"Russakovsky O.","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"S0218001423510023BIB024","first-page":"2579","volume":"9","author":"van der Maaten L.","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"S0218001423510023BIB027","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3400066","volume":"11","author":"Wilson G.","year":"2020","journal-title":"ACM Trans. Intell. Syst. 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