{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T15:19:53Z","timestamp":1748963993738,"version":"3.37.3"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"33","license":[{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18702-1","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T06:02:20Z","timestamp":1709532140000},"page":"79207-79217","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Shared wasserstein adversarial domain adaption"],"prefix":"10.1007","volume":"83","author":[{"given":"Shengqing","family":"Yao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8253-9373","authenticated-orcid":false,"given":"Yuming","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yanfang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhizhong","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Jiaojiao","family":"Ni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"issue":"10","key":"18702_CR1","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 (TKDE) 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng (TKDE)"},{"key":"18702_CR2","unstructured":"Gretton A (2012) A Kernel two-sample test. J Mach Learn Res 13:723\u2013773"},{"key":"18702_CR3","unstructured":"Dziugaite GK, Roy DM, Ghahramani Z (2015) Training generative neural networks via Maximum Mean Discrepancy optimization. Uai"},{"key":"18702_CR4","doi-asserted-by":"crossref","unstructured":"Sun B, Feng J, Saenko K (2016) Returnof frustratingly easy domain adaptation. In: Thirtieth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v30i1.10306"},{"key":"18702_CR5","doi-asserted-by":"crossref","unstructured":"Sun B, Saenko K (2016) Deep CORAL: correlation alignment for deep domain adaptation. In: ICCV workshop on transferring and adapting source knowledge in computer vision (TASK-CV)","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"18702_CR6","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: European conference on computer vision (ECCV), pp 597\u2013613","DOI":"10.1007\/978-3-319-46493-0_36"},{"key":"18702_CR7","doi-asserted-by":"crossref","unstructured":"Huang J, Smola AJ, Gretton A, Borgwardt KM , Scholkopf B (2006) Correcting sample selection bias by unlabeled data. In: NIPS","DOI":"10.7551\/mitpress\/7503.003.0080"},{"key":"18702_CR8","doi-asserted-by":"crossref","unstructured":"Chu W-S, De la Torre F, Cohn JF (2013) Selective transfer machine for personalized facial action unit detection. CVPR","DOI":"10.1109\/CVPR.2013.451"},{"key":"18702_CR9","unstructured":"Jhuo IH, Liu D, Lee DT, Chang SF (2012) Robust visual domain adaptation with low-rank reconstruction. CVPR"},{"key":"18702_CR10","unstructured":"Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. CVPR"},{"key":"18702_CR11","doi-asserted-by":"crossref","unstructured":"Qiu Q, Patel VM, Turaga P, Chellappa R (2012) Domain adaptive dictionary learning. ECCV","DOI":"10.1007\/978-3-642-33765-9_45"},{"key":"18702_CR12","doi-asserted-by":"crossref","unstructured":"Wang X, Shrivastava A, Gupta A (2017) A fast RCNN: hard positive generation via adversary for object detection. In: The IEEE conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2017.324"},{"key":"18702_CR13","unstructured":"Hoffman J, Tzeng E, Park T, Zhu J, Isola P, Saenko K, Efros A, Darrell T (2016) CyCADA: Cycle-Consistent Adversarial Domain Adaptation. In: Dy J, Krause A (eds) Proceedings of the 35th international conference on machine learning (proceedings of machine learning research), vol 80, 1994\u20132003"},{"key":"18702_CR14","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein gan. arXiv:1701.07875"},{"key":"18702_CR15","doi-asserted-by":"crossref","unstructured":"Haeusser P, Frerix T, Mordvintsev A, Cremers D (2018) Associative Domain Adaptation. arXiv:1708.00938","DOI":"10.1109\/ICCV.2017.301"},{"key":"18702_CR16","unstructured":"Donahue J, Kr\u00e4henb\u00fchl P, Darrell T (2016) Adversarial feature learning. arXiv:1605.09782"},{"key":"18702_CR17","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv:1411.1784"},{"key":"18702_CR18","unstructured":"Taigman Y, Polyak A, Wolf L (2016) Unsupervised cross domain image generation. arXiv:1611.02200"},{"key":"18702_CR19","doi-asserted-by":"crossref","unstructured":"Zhu J, Park T, Isola P, Efros AA (2017) Unpaired image to image translation using cycle consistent adversarial networks. In: International conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2017.244"},{"key":"18702_CR20","unstructured":"Liu MY, Tuzel O (2016) Coupled generative adversarial networks. In: Advances in neural information processing systems, pp 469\u2013477"},{"key":"18702_CR21","unstructured":"Liu M, Breuel T, Kautz J (2017) Unsupervised image to image translation networks. arXiv:1703.00848"},{"issue":"19","key":"18702_CR22","doi-asserted-by":"publisher","first-page":"3283","DOI":"10.1049\/iet-ipr.2020.0087","volume":"14","author":"Y Madadi","year":"2020","unstructured":"Madadi Y, Seydi V, Nasrollahi K, Hosseini R, Moeslund T (2020) Deep visual unsupervised domain adaptation for classification tasks: a survey. IET Image Proc 14(19):3283\u20133299","journal-title":"IET Image Proc"},{"key":"18702_CR23","doi-asserted-by":"publisher","unstructured":"Zonoozi MH, Seydi V (2022) A survey on adversarial domain adaptation. Neural Process Lett. https:\/\/doi.org\/10.1007\/s11063-022-10977-5","DOI":"10.1007\/s11063-022-10977-5"},{"key":"18702_CR24","doi-asserted-by":"crossref","unstructured":"LeCun Y (1998) Gradient-based learning applied to document recognition. In: Proceedings of the IEEE 86:22782324","DOI":"10.1109\/5.726791"},{"key":"18702_CR25","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/34.291440","volume":"2016","author":"JJ Hull","year":"1994","unstructured":"Hull JJ (1994) A database for handwritten text recognitionresearch. PAMI 2016:550\u2013554","journal-title":"PAMI"},{"key":"18702_CR26","unstructured":"Netzer Y, Fillet M, Coates A, Bissacco A, Wu B, Ng AY (2011) Reading digits in natural images with unsupervised feature learning. In: NIPS"},{"key":"18702_CR27","doi-asserted-by":"crossref","unstructured":"Saenko K, Kulis B, Fritz M, Darrell T (2010) Adaptingvisual category models to new domains. In: ECCV","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"18702_CR28","doi-asserted-by":"publisher","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems. https:\/\/doi.org\/10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"18702_CR29","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R et al (2009) Imagenet: a large-scale hierarchical image database. Proc of IEEE Computer Vision & Pattern Recognition, 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"18702_CR30","unstructured":"Long M, Zhu H, Wang J, Jordan MI (2017) Deep transfer learning with joint adaptation networks. In: International conference on machine learning, PMLR, pp 2208\u20132217"},{"key":"18702_CR31","unstructured":"Long M, Cao Z, Wang J, Jordan MI (2018) Conditional adversarial domain adaptation. In: Advances in neural information processing systems, pp 1640\u20131650"},{"key":"18702_CR32","doi-asserted-by":"crossref","unstructured":"Pei Z, Cao Z, Long M, Wang J (2019) Multi-adversarial domain adaptation. arXiv:1809.02176","DOI":"10.1609\/aaai.v32i1.11767"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18702-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18702-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18702-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T13:31:11Z","timestamp":1728307871000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18702-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,4]]},"references-count":32,"journal-issue":{"issue":"33","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["18702"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18702-1","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,3,4]]},"assertion":[{"value":"7 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author declares there is no conflict of Interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}