{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T11:00:46Z","timestamp":1761562846137,"version":"build-2065373602"},"reference-count":57,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100007847","name":"Natural Science Foundation of Jilin Province","doi-asserted-by":"publisher","award":["20180101043JC"],"award-info":[{"award-number":["20180101043JC"]}],"id":[{"id":"10.13039\/100007847","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802056"],"award-info":[{"award-number":["61802056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1016\/j.neucom.2021.12.089","type":"journal-article","created":{"date-parts":[[2021,12,31]],"date-time":"2021-12-31T10:43:12Z","timestamp":1640947392000},"page":"462-473","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"special_numbering":"C","title":["Similarity-based domain adaptation network"],"prefix":"10.1016","volume":"493","author":[{"given":"Meixin","family":"Peng","sequence":"first","affiliation":[]},{"given":"Zhanshan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Juan","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2021.12.089_b0005","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"10","key":"10.1016\/j.neucom.2021.12.089_b0010","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Transactions on knowledge and data engineering"},{"year":"2009","series-title":"Dataset shift in machine learning","author":"Qui\u00f1onero-Candela","key":"10.1016\/j.neucom.2021.12.089_b0015"},{"key":"10.1016\/j.neucom.2021.12.089_b0020","unstructured":"F. Zhuang, X. Cheng, P. Luo, S.J. Pan, Q. He, Supervised representation learning: Transfer learning with deep autoencoders, in: Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015."},{"issue":"1","key":"10.1016\/j.neucom.2021.12.089_b0025","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proceedings of the IEEE"},{"key":"10.1016\/j.neucom.2021.12.089_b0030","series-title":"2012 IEEE conference on computer vision and pattern recognition","first-page":"2066","article-title":"Geodesic flow kernel for unsupervised domain adaptation","author":"Gong","year":"2012"},{"key":"10.1016\/j.neucom.2021.12.089_b0035","series-title":"Proceedings of the IEEE international conference on computer vision","first-page":"2200","article-title":"Transfer feature learning with joint distribution adaptation","author":"Long","year":"2013"},{"issue":"2","key":"10.1016\/j.neucom.2021.12.089_b0040","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":"2010","journal-title":"IEEE Transactions on Neural Networks"},{"key":"10.1016\/j.neucom.2021.12.089_b0045","first-page":"601","article-title":"Correcting sample selection bias by unlabeled data","volume":"19","author":"Huang","year":"2006","journal-title":"Advances in neural information processing systems"},{"key":"10.1016\/j.neucom.2021.12.089_b0050","unstructured":"Y. Ganin, V. Lempitsky, Unsupervised domain adaptation by backpropagation, in: International conference on machine learning, PMLR, 2015, pp. 1180\u20131189."},{"key":"10.1016\/j.neucom.2021.12.089_b0055","series-title":"International conference on machine learning PMLR","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","author":"Long","year":"2015"},{"key":"10.1016\/j.neucom.2021.12.089_b0060","unstructured":"M. Long, Z. Cao, J. Wang, M.I. Jordan, Conditional adversarial domain adaptation, arXiv preprint arXiv:1705.10667."},{"key":"10.1016\/j.neucom.2021.12.089_b0065","doi-asserted-by":"crossref","unstructured":"Z. Pei, Z. Cao, M. Long, J. Wang, Multi-adversarial domain adaptation, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32, 2018.","DOI":"10.1609\/aaai.v32i1.11767"},{"key":"10.1016\/j.neucom.2021.12.089_b0070","doi-asserted-by":"crossref","unstructured":"B. Sun, K. Saenko, Deep coral: Correlation alignment for deep domain adaptation, in: European conference on computer vision, Springer, 2016, pp. 443\u2013450.","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"10.1016\/j.neucom.2021.12.089_b0075","unstructured":"Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, V. Lempitsky, Domain-adversarial training of neural networks, The journal of machine learning research 17 (1) (2016) 2096\u20132030."},{"key":"10.1016\/j.neucom.2021.12.089_b0080","unstructured":"Y. Zhu, F. Zhuang, J. Wang, G. Ke, J. Chen, J. Bian, H. Xiong, Q. He, Deep subdomain adaptation network for image classification, IEEE transactions on neural networks and learning systems."},{"key":"10.1016\/j.neucom.2021.12.089_b0085","unstructured":"A. Kumar, P. Sattigeri, K. Wadhawan, L. Karlinsky, R. Feris, W.T. Freeman, G. Wornell, Co-regularized alignment for unsupervised domain adaptation, arXiv preprint arXiv:1811.05443."},{"key":"10.1016\/j.neucom.2021.12.089_b0090","series-title":"2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)","first-page":"1","article-title":"Stratified transfer learning for cross-domain activity recognition","author":"Wang","year":"2018"},{"key":"10.1016\/j.neucom.2021.12.089_b0095","series-title":"International conference on machine learning PMLR","first-page":"5423","article-title":"Learning semantic representations for unsupervised domain adaptation","author":"Xie","year":"2018"},{"key":"10.1016\/j.neucom.2021.12.089_b0100","series-title":"International conference on machine learning PMLR","first-page":"647","article-title":"Decaf: A deep convolutional activation feature for generic visual recognition","author":"Donahue","year":"2014"},{"key":"10.1016\/j.neucom.2021.12.089_b0105","unstructured":"J. Yosinski, J. Clune, Y. Bengio, H. Lipson, How transferable are features in deep neural networks?, arXiv preprint arXiv:1411.1792."},{"key":"10.1016\/j.neucom.2021.12.089_b0110","series-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2970","article-title":"Distant supervised centroid shift: A simple and efficient approach to visual domain adaptation","author":"Liang","year":"2019"},{"key":"10.1016\/j.neucom.2021.12.089_b0115","series-title":"Proceedings of the 26th ACM international conference on Multimedia","first-page":"402","article-title":"Visual domain adaptation with manifold embedded distribution alignment","author":"Wang","year":"2018"},{"issue":"1","key":"10.1016\/j.neucom.2021.12.089_b0120","first-page":"1","article-title":"Transfer learning with dynamic distribution adaptation","volume":"11","author":"Wang","year":"2020","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"10.1016\/j.neucom.2021.12.089_b0125","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neunet.2019.07.010","article-title":"Multi-representation adaptation network for cross-domain image classification","volume":"119","author":"Zhu","year":"2019","journal-title":"Neural Networks"},{"key":"10.1016\/j.neucom.2021.12.089_b0130","unstructured":"G. Kang, J. Lu, Y. Yi, A.G. Hauptmann, Contrastive adaptation network for unsupervised domain adaptation."},{"key":"10.1016\/j.neucom.2021.12.089_b0135","doi-asserted-by":"crossref","first-page":"4260","DOI":"10.1109\/TIP.2018.2839528","article-title":"Domain invariant and class discriminative feature learning for visual domain adaptation","volume":"27","author":"Li","year":"2018","journal-title":"IEEE Trans Image Process"},{"key":"10.1016\/j.neucom.2021.12.089_b0140","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1109\/TPAMI.2018.2868685","article-title":"Transferable representation learning with deep adaptation networks","volume":"41","author":"Long","year":"2018","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neucom.2021.12.089_b0145","series-title":"International conference on machine learning PMLR","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","author":"Long","year":"2017"},{"key":"10.1016\/j.neucom.2021.12.089_b0150","doi-asserted-by":"crossref","unstructured":"Y. Zhu, F. Zhuang, D. Wang, Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 5989\u20135996.","DOI":"10.1609\/aaai.v33i01.33015989"},{"key":"10.1016\/j.neucom.2021.12.089_b0155","unstructured":"W. Zellinger, T. Grubinger, E. Lughofer, T. Natschl\u00e4ger, S. Saminger-Platz, Central moment discrepancy (cmd) for domain-invariant representation learning, arXiv preprint arXiv:1702.08811."},{"key":"10.1016\/j.neucom.2021.12.089_b0160","series-title":"International conference on machine learning PMLR","first-page":"1989","article-title":"Cycada: Cycle-consistent adversarial domain adaptation","author":"Hoffman","year":"2018"},{"key":"10.1016\/j.neucom.2021.12.089_b0165","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"7167","article-title":"Adversarial discriminative domain adaptation","author":"Tzeng","year":"2017"},{"key":"10.1016\/j.neucom.2021.12.089_b0170","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"3801","article-title":"Collaborative and adversarial network for unsupervised domain adaptation","author":"Zhang","year":"2018"},{"key":"10.1016\/j.neucom.2021.12.089_b0175","unstructured":"H. Wang, W. Yang, J. Wang, R. Wang, L. Lan, M. Geng, Pairwise similarity regularization for adversarial domain adaptation, Proceedings of the 28th ACM International Conference on Multimedia."},{"key":"10.1016\/j.neucom.2021.12.089_b0180","doi-asserted-by":"crossref","unstructured":"R. Zhu, X. Jiang, J. Lu, S. Li, Transferable feature learning on graphs across visual domains, 2021 IEEE International Conference on Multimedia and Expo (ICME).","DOI":"10.1109\/ICME51207.2021.9428079"},{"key":"10.1016\/j.neucom.2021.12.089_b0185","unstructured":"S. Li, C.H. Liu, Q. Lin, Q. Wen, L. Su, G. Huang, Z. Ding, Deep residual correction network for partial domain adaptation, IEEE Transactions on Pattern Analysis and Machine Intelligence."},{"key":"10.1016\/j.neucom.2021.12.089_b0190","doi-asserted-by":"crossref","unstructured":"Y. Jin, X. Wang, M. Long, J. Wang, Minimum class confusion for versatile domain adaptation, in: European Conference on Computer Vision (ECCV), 2020.","DOI":"10.1007\/978-3-030-58589-1_28"},{"key":"10.1016\/j.neucom.2021.12.089_b0195","unstructured":"J. Liang, D. Hu, J. Feng, Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation, in: ICML, 2020."},{"key":"10.1016\/j.neucom.2021.12.089_b0200","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1109\/TPAMI.2018.2832198","article-title":"Aggregating randomized clustering-promoting invariant projections for domain adaptation","volume":"41","author":"Liang","year":"2019","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neucom.2021.12.089_b0205","unstructured":"E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, T. Darrell, Deep domain confusion: Maximizing for domain invariance, arXiv preprint arXiv:1412.3474."},{"key":"10.1016\/j.neucom.2021.12.089_b0210","first-page":"248","article-title":"Imagenet: A large-scale hierarchical image database, in: 2009 IEEE conference on computer vision and pattern recognition","volume":"2009","author":"Deng","year":"2009","journal-title":"Ieee"},{"key":"10.1016\/j.neucom.2021.12.089_b0215","doi-asserted-by":"crossref","unstructured":"Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, V. Lempitsky, Domain-adversarial training of neural networks, Journal of Machine Learning Research 17 (1) (2017) 2096\u20132030.","DOI":"10.1007\/978-3-319-58347-1_10"},{"key":"10.1016\/j.neucom.2021.12.089_b0220","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"9944","article-title":"Cluster alignment with a teacher for unsupervised domain adaptation","author":"Deng","year":"2019"},{"key":"10.1016\/j.neucom.2021.12.089_b0225","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"5031","article-title":"Domain-symmetric networks for adversarial domain adaptation","author":"Zhang","year":"2019"},{"key":"10.1016\/j.neucom.2021.12.089_b0230","doi-asserted-by":"crossref","unstructured":"S. Li, C. Liu, Q. Lin, B. Xie, Z. Ding, G. Huang, J. Tang, Domain conditioned adaptation network, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 11386\u201311393.","DOI":"10.1609\/aaai.v34i07.6801"},{"key":"10.1016\/j.neucom.2021.12.089_b0235","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9471","article-title":"Unsupervised domain adaptation using feature-whitening and consensus loss","author":"Roy","year":"2019"},{"key":"10.1016\/j.neucom.2021.12.089_b0240","unstructured":"Y. Zhang, T. Liu, M. Long, M.I. Jordan, Bridging theory and algorithm for domain adaptation."},{"key":"10.1016\/j.neucom.2021.12.089_b0245","series-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"605","article-title":"Adversarial dual distinct classifiers for unsupervised domain adaptation","author":"Jing","year":"2021"},{"key":"10.1016\/j.neucom.2021.12.089_b0250","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"3723","article-title":"Maximum classifier discrepancy for unsupervised domain adaptation","author":"Saito","year":"2018"},{"key":"10.1016\/j.neucom.2021.12.089_b0255","series-title":"Proceedings of the 27th ACM International Conference on Multimedia","first-page":"729","article-title":"Joint adversarial domain adaptation","author":"Li","year":"2019"},{"key":"10.1016\/j.neucom.2021.12.089_b0260","doi-asserted-by":"crossref","unstructured":"H. Tang, K. Jia, Discriminative adversarial domain adaptation, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 5940\u20135947.","DOI":"10.1609\/aaai.v34i04.6054"},{"key":"10.1016\/j.neucom.2021.12.089_b0265","unstructured":"K. Saenko, B. Kulis, M. Fritz, T. Darrell, T.: Adapting visual category models to new domains. in: Eccv, Springer, Berlin, Heidelberg."},{"key":"10.1016\/j.neucom.2021.12.089_b0270","unstructured":"H. Venkateswara, J. Eusebio, S. Chakraborty, S. Panchanathan, Deep hashing network for unsupervised domain adaptation, IEEE Computer Society."},{"key":"10.1016\/j.neucom.2021.12.089_b0275","unstructured":"X. Peng, B. Usman, N. Kaushik, J. Hoffman, K. Saenko, Visda: The visual domain adaptation challenge."},{"key":"10.1016\/j.neucom.2021.12.089_b0280","unstructured":"K. He, X. Zhang, S. Ren, S. Jian, Identity mappings in deep residual networks, European Conference on Computer Vision."},{"key":"10.1016\/j.neucom.2021.12.089_b0285","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"J. Mach. Learn. Res."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231221019457?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231221019457?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T20:39:50Z","timestamp":1760387990000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231221019457"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":57,"alternative-id":["S0925231221019457"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2021.12.089","relation":{},"ISSN":["0925-2312"],"issn-type":[{"type":"print","value":"0925-2312"}],"subject":[],"published":{"date-parts":[[2022,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Similarity-based domain adaptation network","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2021.12.089","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}