{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:27:38Z","timestamp":1740122858690,"version":"3.37.3"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T00:00:00Z","timestamp":1609804800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T00:00:00Z","timestamp":1609804800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Key R &D Program of China","award":["2018YFC0309400"],"award-info":[{"award-number":["2018YFC0309400"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871188"],"award-info":[{"award-number":["61871188"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangzhou city science and technology research projects","award":["201902020008"],"award-info":[{"award-number":["201902020008"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s11042-020-10193-0","type":"journal-article","created":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T11:03:18Z","timestamp":1609844598000},"page":"11255-11272","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Domain compensatory adversarial networks for partial domain adaptation"],"prefix":"10.1007","volume":"80","author":[{"given":"Junchu","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4040-0175","authenticated-orcid":false,"given":"Zhiheng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kefeng","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,5]]},"reference":[{"issue":"1\u20132","key":"10193_CR1","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":"10193_CR2","unstructured":"Blitzer J, Crammer K, Kulesza A, Pereira F, Wortman J (2008) Learning bounds for domain adaptation. In: Advances in neural information processing systems, pp 129\u2013136"},{"key":"10193_CR3","doi-asserted-by":"crossref","unstructured":"Cao Z, Long M, Wang J, Jordan MI (2018) Partial transfer learning with selective adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2724\u20132732","DOI":"10.1109\/CVPR.2018.00288"},{"key":"10193_CR4","doi-asserted-by":"crossref","unstructured":"Cao Z, Ma L, Long M, Wang J (2018) Partial adversarial domain adaptation. In: Proceedings of the European conference on computer vision (ECCV), pp 135\u2013150","DOI":"10.1007\/978-3-030-01237-3_9"},{"key":"10193_CR5","doi-asserted-by":"crossref","unstructured":"Cariucci FM, Porzi L, Caputo B, Ricci E, Bulo SR (2017) Autodial: automatic domain alignment layers. In: 2017 IEEE international conference on computer vision (ICCV). IEEE, pp 5077\u20135085","DOI":"10.1109\/ICCV.2017.542"},{"key":"10193_CR6","doi-asserted-by":"crossref","unstructured":"Chen C, Chen Z, Jiang B, Jin X (2019) Joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 3296\u20133303","DOI":"10.1609\/aaai.v33i01.33013296"},{"key":"10193_CR7","unstructured":"Chen X, Wang S, Long M, Wang J (2019) Transferability vs. discriminability: batch spectral penalization for adversarial domain adaptation. In: International conference on machine learning, pp 1081\u20131090"},{"key":"10193_CR8","doi-asserted-by":"crossref","unstructured":"Deng Z, Luo Y, Zhu J (2019) Cluster alignment with a teacher for unsupervised domain adaptation. In: Proceedings of the IEEE international conference on computer vision, pp 9944\u20139953","DOI":"10.1109\/ICCV.2019.01004"},{"issue":"1","key":"10193_CR9","first-page":"2096","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2096\u20132030","journal-title":"J Mach Learn Res"},{"key":"10193_CR10","unstructured":"Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: IEEE conference on computer vision and pattern recognition, pp 2066\u20132073"},{"key":"10193_CR11","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672\u20132680"},{"key":"10193_CR12","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10193_CR13","doi-asserted-by":"crossref","unstructured":"Hoffman J, Darrell T, Saenko K (2014) Continuous manifold based adaptation for evolving visual domains. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 867\u2013874","DOI":"10.1109\/CVPR.2014.116"},{"key":"10193_CR14","unstructured":"Kumar A, Sattigeri P, Wadhawan K, Karlinsky L, Feris R, Freeman B, Wornell G (2018) Co-regularized alignment for unsupervised domain adaptation. In: Advances in neural information processing systems, pp 9345\u20139356"},{"key":"10193_CR15","doi-asserted-by":"crossref","unstructured":"Kurmi VK, Kumar S, Namboodiri VP (2019) Attending to discriminative certainty for domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 491\u2013500","DOI":"10.1109\/CVPR.2019.00058"},{"key":"10193_CR16","doi-asserted-by":"crossref","unstructured":"Lee S, Kim D, Kim N, Jeong SG (2019) Drop to adapt: learning discriminative features for unsupervised domain adaptation. In: Proceedings of the IEEE international conference on computer vision, pp 91\u2013100","DOI":"10.1109\/ICCV.2019.00018"},{"key":"10193_CR17","doi-asserted-by":"crossref","unstructured":"LeTien N, Habrard A, Sebban M (2019) Differentially private optimal transport: application to domain adaptation. In: IJCAI, pp 2852\u20132858","DOI":"10.24963\/ijcai.2019\/395"},{"issue":"9","key":"10193_CR18","doi-asserted-by":"publisher","first-page":"4260","DOI":"10.1109\/TIP.2018.2839528","volume":"27","author":"S Li","year":"2018","unstructured":"Li S, Song S, Huang G, Ding Z, Wu C (2018) Domain invariant and class discriminative feature learning for visual domain adaptation. IEEE Trans Image Process 27(9):4260\u20134273","journal-title":"IEEE Trans Image Process"},{"issue":"12","key":"10193_CR19","doi-asserted-by":"publisher","first-page":"6103","DOI":"10.1109\/TIP.2019.2924174","volume":"28","author":"J Li","year":"2019","unstructured":"Li J, Jing M, Lu K, Zhu L, Shen HT (2019) Locality preserving joint transfer for domain adaptation. IEEE Trans Image Process 28(12):6103\u20136115","journal-title":"IEEE Trans Image Process"},{"key":"10193_CR20","doi-asserted-by":"crossref","unstructured":"Li K, Wigington C, Tensmeyer C, Zhao H, Barmpalios N, Morariu V I, Manjunatha V, Sun T, Fu Y (2020) Cross-domain document object detection: Benchmark suite and method. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 12,915\u201312,924","DOI":"10.1109\/CVPR42600.2020.01293"},{"key":"10193_CR21","doi-asserted-by":"crossref","unstructured":"Li M, Zhai Y M, Luo YW, Ge PF, Ren CX (2020) Enhanced transport distance for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 13,936\u201313,944","DOI":"10.1109\/CVPR42600.2020.01395"},{"key":"10193_CR22","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.neunet.2020.06.014","volume":"129","author":"X Li","year":"2020","unstructured":"Li X, Zhang W, Ma H, Luo Z, Li X (2020) Partial transfer learning in machinery cross-domain fault diagnostics using class-weighted adversarial networks. Neural Netw 129:313\u2013322","journal-title":"Neural Netw"},{"key":"10193_CR23","doi-asserted-by":"crossref","unstructured":"Liang J, He R, Sun Z, Tan T (2019) Distant supervised centroid shift: a simple and efficient approach to visual domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2975\u20132984","DOI":"10.1109\/CVPR.2019.00309"},{"issue":"5","key":"10193_CR24","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1109\/TKDE.2013.111","volume":"26","author":"M Long","year":"2013","unstructured":"Long M, Wang J, Ding G, Pan SJ, Philip SY (2013) Adaptation regularization: a general framework for transfer learning. IEEE Trans Knowl Data Eng 26(5):1076\u20131089","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10193_CR25","doi-asserted-by":"crossref","unstructured":"Long M, Wang J, Ding G, Sun J, Yu PS (2013) Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE international conference on computer vision, pp 2200\u20132207","DOI":"10.1109\/ICCV.2013.274"},{"key":"10193_CR26","unstructured":"Long M, Cao Y, Wang J, Jordan M (2015) Learning transferable features with deep adaptation networks. In: International conference on machine learning, pp 97\u2013105"},{"key":"10193_CR27","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":"10193_CR28","unstructured":"Long M, Zhu H, Wang J, Jordan MI (2017) Deep transfer learning with joint adaptation networks. In: International conference on machine learning, pp 2208\u20132217"},{"key":"10193_CR29","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":"10193_CR30","first-page":"2579","volume":"9","author":"Lvd Maaten","year":"2008","unstructured":"Maaten Lvd, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9:2579\u20132605","journal-title":"J Mach Learn Res"},{"issue":"10","key":"10193_CR31","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"10193_CR32","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Tsang IW, Kwok JT, Yang Q (2010) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199\u2013210","journal-title":"IEEE Trans Neural Netw"},{"key":"10193_CR33","doi-asserted-by":"crossref","unstructured":"Pinheiro PO (2018) Unsupervised domain adaptation with similarity learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8004\u20138013","DOI":"10.1109\/CVPR.2018.00835"},{"issue":"3","key":"10193_CR34","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"key":"10193_CR35","doi-asserted-by":"crossref","unstructured":"Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: European conference on computer vision. Springer, pp 213\u2013226","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"10193_CR36","doi-asserted-by":"crossref","unstructured":"Saito K, Watanabe K, Ushiku Y, Harada T (2018) Maximum classifier discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3723\u20133732","DOI":"10.1109\/CVPR.2018.00392"},{"key":"10193_CR37","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"10193_CR38","doi-asserted-by":"crossref","unstructured":"Sun B, Saenko K (2016) Deep coral: correlation alignment for deep domain adaptation. In: European conference on computer vision. Springer, pp 443\u2013450","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"10193_CR39","doi-asserted-by":"crossref","unstructured":"Tang H, Chen K, Jia K (2020) Unsupervised domain adaptation via structurally regularized deep clustering. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8725\u20138735","DOI":"10.1109\/CVPR42600.2020.00875"},{"key":"10193_CR40","unstructured":"Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (2014) Deep domain confusion: maximizing for domain invariance. arXiv:1412.3474"},{"key":"10193_CR41","doi-asserted-by":"crossref","unstructured":"Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7167\u20137176","DOI":"10.1109\/CVPR.2017.316"},{"key":"10193_CR42","doi-asserted-by":"crossref","unstructured":"Venkateswara H, Eusebio J, Chakraborty S, Panchanathan S (2017) Deep hashing network for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5018\u20135027","DOI":"10.1109\/CVPR.2017.572"},{"key":"10193_CR43","doi-asserted-by":"crossref","unstructured":"Volpi R, Morerio P, Savarese S, Murino V (2018) Adversarial feature augmentation for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5495\u20135504","DOI":"10.1109\/CVPR.2018.00576"},{"key":"10193_CR44","doi-asserted-by":"crossref","unstructured":"Wang J, Chen Y, Yu H, Huang M, Yang Q (2019) Easy transfer learning by exploiting intra-domain structures. In: IEEE international conference on multimedia and expo, pp 1210\u20131215","DOI":"10.1109\/ICME.2019.00211"},{"key":"10193_CR45","doi-asserted-by":"crossref","unstructured":"Wang S, Chen X, Wang Y, Long M, Wang J (2020) Progressive adversarial networks for fine-grained domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9213\u20139222","DOI":"10.1109\/CVPR42600.2020.00923"},{"key":"10193_CR46","unstructured":"Xie S, Zheng Z, Chen L, Chen C (2018) Learning semantic representations for unsupervised domain adaptation. In: International conference on machine learning, pp 5423\u20135432"},{"key":"10193_CR47","doi-asserted-by":"crossref","unstructured":"Xie S, Kirillov A, Girshick R, He K (2019) Exploring randomly wired neural networks for image recognition. In: Proceedings of the IEEE international conference on computer vision, pp 1284\u20131293","DOI":"10.1109\/ICCV.2019.00137"},{"key":"10193_CR48","doi-asserted-by":"crossref","unstructured":"Yan H, Ding Y, Li P, Wang Q, Xu Y, Zuo W (2017) Mind the class weight bias: weighted maximum mean discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2272\u20132281","DOI":"10.1109\/CVPR.2017.107"},{"key":"10193_CR49","unstructured":"Zellinger W, Grubinger T, Lughofer E, Natschl\u00e4ger T, Saminger-Platz S (2017) Central moment discrepancy (cmd) for domain-invariant representation learning. arXiv:1702.08811"},{"key":"10193_CR50","doi-asserted-by":"crossref","unstructured":"Zhang J, Ding Z, Li W, Ogunbona P (2018) Importance weighted adversarial nets for partial domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8156\u20138164","DOI":"10.1109\/CVPR.2018.00851"},{"key":"10193_CR51","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tang H, Jia K, Tan M (2019) Domain-symmetric networks for adversarial domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5031\u20135040","DOI":"10.1109\/CVPR.2019.00517"},{"key":"10193_CR52","unstructured":"Zohrizadeh F, Kheirandishfard M, Kamangar F (2019) Class subset selection for partial domain adaptation. In: CVPR Workshops"},{"key":"10193_CR53","doi-asserted-by":"crossref","unstructured":"Zou H, Zhou Y, Yang J, Liu H, Das HP, Spanos CJ (2019) Consensus adversarial domain adaptation. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 5997\u20136004","DOI":"10.1609\/aaai.v33i01.33015997"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10193-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-020-10193-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10193-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T15:48:24Z","timestamp":1670687304000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-020-10193-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,5]]},"references-count":53,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["10193"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-10193-0","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2021,1,5]]},"assertion":[{"value":"6 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}