{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T18:50:30Z","timestamp":1775933430307,"version":"3.50.1"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2016,8,1]],"date-time":"2016-08-01T00:00:00Z","timestamp":1470009600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61325008"],"award-info":[{"award-number":["61325008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502265"],"award-info":[{"award-number":["61502265"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2015T80088"],"award-info":[{"award-number":["2015T80088"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science and Technology Supporting Program","award":["2015BAH14F02"],"award-info":[{"award-number":["2015BAH14F02"]}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["III-1526499"],"award-info":[{"award-number":["III-1526499"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tsinghua National Laboratory (TNList) Special Fund for Big Data Science and Technology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2016,8,1]]},"DOI":"10.1109\/tkde.2016.2554549","type":"journal-article","created":{"date-parts":[[2016,4,14]],"date-time":"2016-04-14T18:10:23Z","timestamp":1460657423000},"page":"2027-2040","source":"Crossref","is-referenced-by-count":162,"title":["Deep Learning of Transferable Representation for Scalable Domain Adaptation"],"prefix":"10.1109","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9421-463X","authenticated-orcid":false,"given":"Mingsheng","family":"Long","sequence":"first","affiliation":[]},{"given":"Jianmin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Jiaguang","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"410","article-title":"Learning with marginalized corrupted features","author":"maaten","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref38","first-page":"153","article-title":"Greedy layer-wise training of deep networks","author":"bengio","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref33","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","author":"long","year":"0","journal-title":"Proceedings of the 32nd Intl Conf on Machine Learning"},{"key":"ref32","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","author":"ganin","year":"0","journal-title":"Proceedings of the 32nd Intl Conf on Machine Learning"},{"key":"ref31","article-title":"Deep domain confusion: Maximizing for domain invariance","author":"tzeng","year":"2014"},{"key":"ref30","first-page":"3536","article-title":"LSDA: Large scale detection through adaptation","author":"hoffman","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.111"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1214\/13-AOS1140"},{"key":"ref34","first-page":"4119","article-title":"Supervised representation learning: Transfer learning with deep autoencoders","author":"zhuang","year":"0","journal-title":"Proc 24th Int Joint Conf Artif Intell"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref27","first-page":"2456","article-title":"Co-training for domain adaptation","author":"chen","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref29","first-page":"2066","article-title":"Geodesic flow kernel for unsupervised domain adaptation","author":"gong","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recog"},{"key":"ref2","first-page":"440","article-title":"Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification","author":"blitzer","year":"0","journal-title":"Proc Annual Meeting of the Assoc Computational Linguistics"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref20","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"vincent","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref22","first-page":"755","article-title":"B-test: A non-parametric, low variance kernel two-sample test","author":"zaremba","year":"0","journal-title":"Proc Adv Neural Inform Process Syst"},{"key":"ref21","first-page":"318","volume":"1","author":"rumelhart","year":"1986"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2373376"},{"key":"ref23","first-page":"1425","article-title":"Covariate shift in hilbert space: A solution via surrogate kernels","author":"zhang","year":"0","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339730"},{"key":"ref25","first-page":"150","article-title":"From bias to opinion: A transfer-learning approach to real-time sentiment analysis","author":"guerra","year":"0","journal-title":"Proc 17th ACM SIGKDD Int Conf Knowl Discovery Data Mining"},{"key":"ref10","first-page":"1898","article-title":"Flexible transfer learning under support and model shift","author":"wang","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref40","first-page":"1476","article-title":"Marginalized denoising auto-encoders for nonlinear representations","author":"chen","year":"0","journal-title":"Proc 29th Int Conf Mach Learn"},{"key":"ref12","first-page":"1214","article-title":"Optimal kernel choice for large-scale two-sample tests","author":"gretton","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref13","first-page":"513","article-title":"Domain adaptation for large-scale sentiment classification: A deep learning approach","author":"glorot","year":"0","journal-title":"Proc 28th Int Conf Mach Learn"},{"key":"ref14","first-page":"689","article-title":"Multimodal deep learning","author":"ngiam","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref15","first-page":"767","article-title":"Marginalized denoising autoencoders for domain adaptation","author":"chen","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref16","first-page":"3320","article-title":"How transferable are features in deep neural networks?","author":"yosinski","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref17","first-page":"137","article-title":"Analysis of representations for domain adaptation","author":"ben-david","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref19","first-page":"19","article-title":"Domain adaptation: Learning bounds and algorithms","author":"mansour","year":"0","journal-title":"Proc Conf Comput Learning Theory"},{"key":"ref4","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"gretton","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772767"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","article-title":"Domain adaptation via transfer component analysis","volume":"22","author":"pan","year":"2011","journal-title":"IEEE Trans Neural Netw"},{"key":"ref5","first-page":"601","article-title":"Correcting sample selection bias by unlabeled data","author":"huang","year":"0","journal-title":"Proc Adv Neural Inform Process Syst"},{"key":"ref8","first-page":"819","article-title":"Domain adaptation under target and conditional shift","author":"zhang","year":"0","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.114"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2011.2178556"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref46","first-page":"1750","article-title":"Kernel choice and classifiability for rkhs embeddings of probability distributions","author":"sriperumbudur","year":"2009","journal-title":"Proc 23rd Annu Conf Adv Neural Inf Process Syst"},{"key":"ref45","article-title":"Pylearn2: A machine learning research library","author":"goodfellow","year":"2013"},{"key":"ref48","first-page":"647","article-title":"Decaf: A deep convolutional activation feature for generic visual recognition","author":"donahue","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref47","first-page":"137","article-title":"Analysis of representations for domain adaptation","author":"ben-david","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref42","first-page":"1106","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"0","journal-title":"Proc Adv Neural Inform Process Syst"},{"key":"ref41","first-page":"2213","article-title":"Hybrid heterogeneous transfer learning through deep learning","author":"zhou","year":"0","journal-title":"Proc 28th AAAI Conf Artif Intell"},{"key":"ref44","first-page":"1","article-title":"Exploring strategies for training deep neural networks","volume":"10","author":"larochelle","year":"2009","journal-title":"J Mach Learn Res"},{"key":"ref43","first-page":"129","article-title":"Parsing natural scenes and natural language with recursive neural networks","author":"socher","year":"0","journal-title":"Proc 28th Int Conf Mach Learn"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/7505473\/07452659.pdf?arnumber=7452659","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T16:11:42Z","timestamp":1642003902000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7452659\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,8,1]]},"references-count":49,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2016.2554549","relation":{},"ISSN":["1041-4347"],"issn-type":[{"value":"1041-4347","type":"print"}],"subject":[],"published":{"date-parts":[[2016,8,1]]}}}