{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:46:28Z","timestamp":1768524388372,"version":"3.49.0"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61806039"],"award-info":[{"award-number":["61806039"]}],"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":["61832001"],"award-info":[{"award-number":["61832001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012152","name":"National Postdoctoral Program for Innovative Talents","doi-asserted-by":"publisher","award":["BX201700045"],"award-info":[{"award-number":["BX201700045"]}],"id":[{"id":"10.13039\/501100012152","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M623006"],"award-info":[{"award-number":["2017M623006"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Department of Science and Technology","award":["2019YFG0141"],"award-info":[{"award-number":["2019YFG0141"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1109\/tip.2019.2924174","type":"journal-article","created":{"date-parts":[[2019,6,26]],"date-time":"2019-06-26T20:07:06Z","timestamp":1561579626000},"page":"6103-6115","source":"Crossref","is-referenced-by-count":244,"title":["Locality Preserving Joint Transfer for Domain Adaptation"],"prefix":"10.1109","volume":"28","author":[{"given":"Jingjing","family":"Li","sequence":"first","affiliation":[]},{"given":"Mengmeng","family":"Jing","sequence":"additional","affiliation":[]},{"given":"Ke","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Heng Tao","family":"Shen","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","article-title":"Learning transferable features with deep adaptation networks","author":"long","year":"2015","journal-title":"arXiv 1502 02791"},{"key":"ref38","article-title":"CyCADA: Cycle-consistent adversarial domain adaptation","author":"hoffman","year":"2017","journal-title":"arXiv 1711 03213"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.06.051"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33709-3_50"},{"key":"ref31","first-page":"1286","article-title":"Reshaping visual datasets for domain adaptation","author":"gong","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2868854"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2704624"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2016.7727470"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref62","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref61","article-title":"VisDA: The visual domain adaptation challenge","author":"peng","year":"2017","journal-title":"arXiv 1710 06924"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2609820"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.368"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.387"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2797248"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.107"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91458-9_30"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.97"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.549"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.183"},{"key":"ref21","first-page":"1697","article-title":"Joint feature selection and structure preservation for domain adaptation","author":"li","year":"2016","journal-title":"Proc 25th Int Conf Artif Intell"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"ref23","first-page":"2066","article-title":"Geodesic flow kernel for unsupervised domain adaptation","author":"gong","year":"2012","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2016.2539541"},{"key":"ref25","article-title":"Learning from labeled and unlabeled data with label propagation","author":"zhu","year":"2002"},{"key":"ref50","first-page":"1294","article-title":"Joint feature selection and subspace learning","volume":"22","author":"gu","year":"2011","journal-title":"Proc 22nd Int Joint Conf Artif Intell"},{"key":"ref51","first-page":"513","article-title":"A kernel method for the two-sample-problem","author":"gretton","year":"2007","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/AFGR.2002.1004130"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.438"},{"key":"ref57","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref56","article-title":"Decaf: A deep convolutional activation feature for generic visual recognition","author":"donahue","year":"2013","journal-title":"arXiv 1310 1531"},{"key":"ref55","article-title":"Caltech-256 object category dataset","author":"griffin","year":"2007"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2565898"},{"key":"ref52","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00392"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2814042"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00835"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.167"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.547"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298600"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TPAMI.2005.55","article-title":"Face recognition using Laplacianfaces","volume":"27","author":"he","year":"2005","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2007.89"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.250598"},{"key":"ref18","first-page":"222","article-title":"Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation","author":"gong","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2820174"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2631887"},{"key":"ref3","first-page":"3453","article-title":"Deep low-rank coding for transfer learning","author":"ding","year":"2015","journal-title":"Proc 24th Int Joint Conf Artif Intell"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126344"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2009.57"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2819503"},{"key":"ref49","first-page":"321","article-title":"Learning with local and global consistency","author":"zhou","year":"2004","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","article-title":"Unsupervised domain adaptation by backpropagation","author":"ganin","year":"2014","journal-title":"arXiv 1409 7495"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2598679"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.310"},{"key":"ref45","first-page":"343","article-title":"Domain separation networks","author":"bousmalis","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref48","article-title":"Learning to discover cross-domain relations with generative adversarial networks","author":"kim","year":"2017","journal-title":"arXiv 1703 05192"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_36"},{"key":"ref41","article-title":"Deep domain confusion: Maximizing for domain invariance","author":"tzeng","year":"2014","journal-title":"arXiv 1412 3474"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00400"},{"key":"ref43","first-page":"4119","article-title":"Supervised representation learning: Transfer learning with deep autoencoders","author":"zhuang","year":"2015","journal-title":"Proc 24th Int Joint Conf Artif Intell"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/8821493\/08746823.pdf?arnumber=8746823","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T20:48:55Z","timestamp":1657745335000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8746823\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":66,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tip.2019.2924174","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12]]}}}