{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T16:57:45Z","timestamp":1751475465902,"version":"3.37.3"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T00:00:00Z","timestamp":1587600000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T00:00:00Z","timestamp":1587600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61402238","61502245"],"award-info":[{"award-number":["61402238","61502245"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010035","name":"Outstanding Youth Foundation of Jiangsu Province of China","doi-asserted-by":"publisher","award":["BK20190089"],"award-info":[{"award-number":["BK20190089"]}],"id":[{"id":"10.13039\/501100010035","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1007\/s10044-020-00881-w","type":"journal-article","created":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T10:02:44Z","timestamp":1587636164000},"page":"1665-1675","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Unsupervised visual domain adaptation via discriminative dictionary evolution"],"prefix":"10.1007","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9347-5395","authenticated-orcid":false,"given":"Songsong","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangwei","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuoyong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Yuan","family":"Jing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,23]]},"reference":[{"key":"881_CR1","doi-asserted-by":"crossref","unstructured":"Baktashmotlagh M, Harandi MT, Lovell BC, Salzmann M (2013) Unsupervised domain adaptation by domain invariant projection. In: ICCV, pp 769\u2013776","DOI":"10.1109\/ICCV.2013.100"},{"key":"881_CR2","doi-asserted-by":"crossref","unstructured":"Baktashmotlagh M, Harandi MT, Lovell BC, Salzmann M (2014) Domain adaptation on the statistical manifold. In: CVPR, pp 2481\u20132488","DOI":"10.1109\/CVPR.2014.318"},{"issue":"1\u20132","key":"881_CR3","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","volume":"79","author":"S Ben-David","year":"2010","unstructured":"Ben-David S, Blitzer J, Crammer K, Kulesza A, Pereira F, Vaughan JW (2010) A theory of learning from different domains. Mach Learn 79(1\u20132):151\u2013175","journal-title":"Mach Learn"},{"key":"881_CR4","unstructured":"Chen M, Weinberger K, Sha F, Bengio Y (2014) Marginalized denoising auto-encoders for nonlinear representations. In: ICML, pp\u00a01476\u20131484"},{"key":"881_CR5","unstructured":"Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T (2014) Decaf: a deep convolutional activation feature for generic visual recognition. In: ICML, pp 647\u2013655"},{"issue":"3","key":"881_CR6","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1109\/TPAMI.2011.114","volume":"34","author":"L Duan","year":"2012","unstructured":"Duan L, Tsang IW, Xu D (2012) Domain transfer multiple kernel learning. IEEE Trans Pattern Anal Mach Intell 34(3):465\u2013479","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"881_CR7","doi-asserted-by":"crossref","unstructured":"Duan L, Tsang IW, Xu D, Chua T (2009) Domain adaptation from multiple sources via auxiliary classifiers. In: ICML, pp 289\u2013296","DOI":"10.1145\/1553374.1553411"},{"key":"881_CR8","doi-asserted-by":"crossref","unstructured":"Fernando B, Habrard A, Sebban M, Tuytelaars T (2013) Unsupervised visual domain adaptation using subspace alignment. In: ICCV, pp 2960\u20132967","DOI":"10.1109\/ICCV.2013.368"},{"key":"881_CR9","unstructured":"Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: Proceedings of the 32nd international conference on machine learning, vol 37, pp 1180\u20131189"},{"key":"881_CR10","doi-asserted-by":"crossref","unstructured":"Ghifary M, Kleijn WB, Zhang M, Balduzzi D, Li W (2016) Deep reconstruction-classification networks for unsupervised domain adaptation. In: ECCV, pp 597\u2013613","DOI":"10.1007\/978-3-319-46493-0_36"},{"issue":"1\u20132","key":"881_CR11","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11263-014-0718-4","volume":"109","author":"B Gong","year":"2014","unstructured":"Gong B, Grauman K, Sha F (2014) Learning kernels for unsupervised domain adaptation with applications to visual object recognition. Int J Comput Vis 109(1\u20132):3\u201327","journal-title":"Int J Comput Vis"},{"key":"881_CR12","unstructured":"Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: CVPR, pp 2066\u20132073"},{"key":"881_CR13","doi-asserted-by":"crossref","unstructured":"Gopalan R, Li R, Chellappa R (2011) Domain adaptation for object recognition: an unsupervised approach. In: ICCV, pp 999\u20131006","DOI":"10.1109\/ICCV.2011.6126344"},{"issue":"11","key":"881_CR14","doi-asserted-by":"publisher","first-page":"2288","DOI":"10.1109\/TPAMI.2013.249","volume":"36","author":"R Gopalan","year":"2014","unstructured":"Gopalan R, Li R, Chellappa R (2014) Unsupervised adaptation across domain shifts by generating intermediate data representations. IEEE Trans Pattern Anal Mach Intell 36(11):2288\u20132302","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"881_CR15","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"Gretton A, Borgwardt KM, Rasch MJ, Sch\u00f6lkopf B, Smola A (2012) A kernel two-sample test. J Mach Learn Res 13:723\u2013773","journal-title":"J Mach Learn Res"},{"key":"881_CR16","unstructured":"Griffin G, Holub A, Perona P (2007) Caltech-256 Object Category Dataset. Tech. Rep, California Institute of Technology"},{"issue":"5","key":"881_CR17","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1016\/j.imavis.2009.08.002","volume":"28","author":"R Gross","year":"2010","unstructured":"Gross R, Matthews I, Cohn JF, Kanade T, Baker S (2010) Multi-pie. Image Vis Comput 28(5):807\u2013813","journal-title":"Image Vis Comput"},{"key":"881_CR18","doi-asserted-by":"crossref","unstructured":"Huang D, Wang YF (2013) Coupled dictionary and feature space learning with applications to cross-domain image synthesis and recognition. In: ICCV, pp 2496\u20132503","DOI":"10.1109\/ICCV.2013.310"},{"issue":"5","key":"881_CR19","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/34.291440","volume":"16","author":"JJ Hull","year":"1994","unstructured":"Hull JJ (1994) A database for handwritten text recognition research. IEEE Trans Pattern Anal Mach Intell 16(5):550\u2013554","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"881_CR20","doi-asserted-by":"publisher","first-page":"2651","DOI":"10.1109\/TPAMI.2013.88","volume":"35","author":"Z Jiang","year":"2013","unstructured":"Jiang Z, Lin Z, Davis LS (2013) Label consistent K-SVD: learning a discriminative dictionary for recognition. IEEE Trans Pattern Anal Mach Intell 35(11):2651\u20132664","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1\u20132","key":"881_CR21","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1007\/s11263-013-0693-1","volume":"109","author":"M Kan","year":"2014","unstructured":"Kan M, Wu J, Shan S, Chen X (2014) Domain adaptation for face recognition: targetize source domain bridged by common subspace. Int J Comput Vis 109(1\u20132):94\u2013109","journal-title":"Int J Comput Vis"},{"key":"881_CR22","doi-asserted-by":"crossref","unstructured":"Kulis B, Saenko K, Darrell T (2011) What you saw is not what you get: domain adaptation using asymmetric kernel transforms. In: CVPR, pp 1785\u20131792","DOI":"10.1109\/CVPR.2011.5995702"},{"key":"881_CR23","doi-asserted-by":"crossref","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, pp 2278\u20132324","DOI":"10.1109\/5.726791"},{"issue":"6","key":"881_CR24","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1109\/TPAMI.2013.167","volume":"36","author":"W Li","year":"2014","unstructured":"Li W, Duan L, Xu D, Tsang IW (2014) Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation. IEEE Trans Pattern Anal Mach Intell 36(6):1134\u20131148","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"881_CR25","doi-asserted-by":"crossref","unstructured":"Long M, Ding G, Wang J, Sun J, Guo Y, Yu PS (2013) Transfer sparse coding for robust image representation. In: CVPR, pp 407\u2013414","DOI":"10.1109\/CVPR.2013.59"},{"issue":"8","key":"881_CR26","doi-asserted-by":"publisher","first-page":"2027","DOI":"10.1109\/TKDE.2016.2554549","volume":"28","author":"M Long","year":"2016","unstructured":"Long M, Wang J, Cao Y, Sun J, Yu PS (2016) Deep learning of transferable representation for scalable domain adaptation. IEEE Trans Knowl Data Eng 28(8):2027\u20132040","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"881_CR27","doi-asserted-by":"crossref","unstructured":"Long M, Wang J, Ding G, Sun J, Yu PS (2014) Transfer joint matching for unsupervised domain adaptation. In: CVPR, pp 1410\u20131417","DOI":"10.1109\/CVPR.2014.183"},{"issue":"6","key":"881_CR28","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1109\/TKDE.2014.2373376","volume":"27","author":"M Long","year":"2015","unstructured":"Long M, Wang J, Sun J, Yu PS (2015) Domain invariant transfer kernel learning. IEEE Trans Knowl Data Eng 27(6):1519\u20131532","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"881_CR29","unstructured":"Long M, Zhu H, Wang J, Jordan MI (2016) Unsupervised domain adaptation with residual transfer networks. In: Advances in neural information processing systems, pp 136\u2013144"},{"key":"881_CR30","doi-asserted-by":"crossref","unstructured":"Lu H, Zhang L, Cao Z, Wei W, Xian K, Shen C, van den Hengel A (2017) When unsupervised domain adaptation meets tensor representations. In: ICCV, pp\u00a0\u200b599\u2013608","DOI":"10.1109\/ICCV.2017.72"},{"key":"881_CR31","doi-asserted-by":"crossref","unstructured":"Motiian S, Piccirilli M, Adjeroh DA, Doretto G (2017) Unified deep supervised domain adaptation and generalization. In: ICCV, pp 5715\u200b\u20135725","DOI":"10.1109\/ICCV.2017.609"},{"key":"881_CR32","unstructured":"Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng AY (2011) Reading digits in natural images with unsupervised feature learning. In: NIPS"},{"key":"881_CR33","doi-asserted-by":"crossref","unstructured":"Ni J, Qiu Q, Chellappa R (2013) Subspace interpolation via dictionary learning for unsupervised domain adaptation. In: CVPR, pp 692\u2013699","DOI":"10.1109\/CVPR.2013.95"},{"key":"881_CR34","unstructured":"Pan SJ, Kwok JT, Yang Q (2008) Transfer learning via dimensionality reduction. In: AAAI, pp 677\u2013682"},{"issue":"2","key":"881_CR35","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2011","unstructured":"Pan SJ, Tsang IW, Kwok JT, Yang Q (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199\u2013210","journal-title":"IEEE Trans Neural Netw"},{"issue":"10","key":"881_CR36","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"881_CR37","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2014.2347059","volume":"32","author":"V Patel","year":"2015","unstructured":"Patel V, Gopalan R, Li R, Chellappa R (2015) Visual domain adaptation: a survey of recent advances. IEEE Signal Process Magazine 32(3):53\u201369","journal-title":"IEEE Signal Process Magazine"},{"key":"881_CR38","doi-asserted-by":"crossref","unstructured":"Qiu Q, Patel VM, Turaga PK, Chellappa R (2012) Domain adaptive dictionary learning. In: ECCV, pp 631\u2013645","DOI":"10.1007\/978-3-642-33765-9_45"},{"key":"881_CR39","doi-asserted-by":"crossref","unstructured":"Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: ECCV, pp 213\u2013226","DOI":"10.1007\/978-3-642-15561-1_16"},{"issue":"10","key":"881_CR40","doi-asserted-by":"publisher","first-page":"2941","DOI":"10.1109\/TIP.2015.2431440","volume":"24","author":"S Shekhar","year":"2015","unstructured":"Shekhar S, Patel VM, Nguyen HV, Chellappa R (2015) Coupled projections for adaptation of dictionaries. IEEE Trans Image Process 24(10):2941\u20132954","journal-title":"IEEE Trans Image Process"},{"key":"881_CR41","unstructured":"Shi Y, Sha F (2012) Information-theoretical learning of discriminative clusters for unsupervised domain adaptation. In: ICML, pp 1275\u200b\u20131282"},{"key":"881_CR42","doi-asserted-by":"crossref","unstructured":"Shu L, Ma T, Latecki LJ (2014) Locality preserving projection for domain adaptation with multi-objective learning. In: AAAI, pp 2085\u20132091","DOI":"10.1609\/aaai.v28i1.9000"},{"issue":"3","key":"881_CR43","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1007\/s11063-015-9494-6","volume":"44","author":"H Sun","year":"2016","unstructured":"Sun H, Liu S, Zhou S (2016) Discriminative subspace alignment for unsupervised visual domain adaptation. Neural Process Lett 44(3):779\u2013793","journal-title":"Neural Process Lett"},{"key":"881_CR44","doi-asserted-by":"crossref","unstructured":"Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: CVPR, pp\u00a0\u200b7167\u20137176","DOI":"10.1109\/CVPR.2017.316"},{"key":"881_CR45","unstructured":"Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (2014) Deep domain confusion: maximizing for domain invariance. ArXiv preprint arXiv:1412.3474 (2014)"},{"key":"881_CR46","doi-asserted-by":"crossref","unstructured":"Volpi R, Morerio P, Savarese S, Murino V (2018) Adversarial feature augmentation for unsupervised domain adaptation. In: CVPR, pp 5495\u200b\u20135504","DOI":"10.1109\/CVPR.2018.00576"},{"key":"881_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-018-9822-8","author":"J Wang","year":"2018","unstructured":"Wang J, Li X, Du J (2018) Label space embedding of manifold alignment for domain adaption. Neural Process Lett. https:\/\/doi.org\/10.1007\/s11063-018-9822-8","journal-title":"Neural Process Lett"},{"key":"881_CR48","doi-asserted-by":"crossref","unstructured":"Wu S, Jing XY, Yue D, Zhang J, Yang J, Yang J (2016) Unsupervised visual domain adaptation via dictionary evolution. In: 2016 IEEE international conference on multimedia and expo (ICME), pp 1\u20136","DOI":"10.1109\/ICME.2016.7552896"},{"key":"881_CR49","unstructured":"Xiao M, Guo Y (2012) Semi-supervised kernel matching for domain adaptation. In: AAAI, pp 1183\u200b\u20131189"},{"key":"881_CR50","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-017-9775-3","author":"X Xie","year":"2017","unstructured":"Xie X, Sun S, Chen H, Qian J (2017) Domain adaptation with twin support vector machines. Neural Process Lett. https:\/\/doi.org\/10.1007\/s11063-017-9775-3","journal-title":"Neural Process Lett"},{"key":"881_CR51","doi-asserted-by":"crossref","unstructured":"Xu X, Shimada A, Taniguchi R, He L (2015) Coupled dictionary learning and feature mapping for cross-modal retrieval. In: ICME, pp 1\u20136","DOI":"10.1109\/ICME.2015.7177396"},{"issue":"2","key":"881_CR52","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/TIP.2015.2510498","volume":"25","author":"Y Xu","year":"2016","unstructured":"Xu Y, Fang X, Wu J, Li X, Zhang D (2016) Discriminative transfer subspace learning via low-rank and sparse representation. IEEE Trans Image Process 25(2):850\u2013863","journal-title":"IEEE Trans Image Process"},{"key":"881_CR53","doi-asserted-by":"crossref","unstructured":"Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. In: ICCV, pp 543\u2013550","DOI":"10.1109\/ICCV.2011.6126286"},{"key":"881_CR54","doi-asserted-by":"crossref","unstructured":"Zadrozny B (2004) Learning and evaluating classifiers under sample selection bias. In: ICML, pp 114\u200b\u2013122","DOI":"10.1145\/1015330.1015425"},{"key":"881_CR55","unstructured":"Zelnik-Manor L, Perona P (2004) Self-tuning spectral clustering. In: NIPS, pp 1601\u20131608"},{"issue":"7","key":"881_CR56","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1109\/TPAMI.2012.239","volume":"35","author":"XY Zhang","year":"2013","unstructured":"Zhang XY, Liu CL (2013) Writer adaptation with style transfer mapping. IEEE Trans Pattern Anal Mach Intell 35(7):1773\u20131787","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"881_CR57","doi-asserted-by":"crossref","unstructured":"Zheng J, Jiang Z, Phillips PJ, Chellappa R (2012) Cross-view action recognition via a transferable dictionary pair. In: BMVC, pp 1\u201311","DOI":"10.5244\/C.26.125"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-020-00881-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-020-00881-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-020-00881-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T03:33:42Z","timestamp":1666409622000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-020-00881-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,23]]},"references-count":57,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["881"],"URL":"https:\/\/doi.org\/10.1007\/s10044-020-00881-w","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"type":"print","value":"1433-7541"},{"type":"electronic","value":"1433-755X"}],"subject":[],"published":{"date-parts":[[2020,4,23]]},"assertion":[{"value":"18 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}