{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:37:58Z","timestamp":1765233478078,"version":"3.37.3"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFA0714103"],"award-info":[{"award-number":["2020YFA0714103"]}]},{"name":"Innovation Capacity Construction Project of Jilin Province Development and Reform Commission","award":["2021FGWCXNLJSSZ10","2019C053-3"],"award-info":[{"award-number":["2021FGWCXNLJSSZ10","2019C053-3"]}]},{"name":"Fundamental Research Funds for the Central Universities,JLU"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s10489-022-04262-0","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T02:47:48Z","timestamp":1675997268000},"page":"18790-18805","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Sample separation and domain alignment complementary learning mechanism for open set domain adaptation"],"prefix":"10.1007","volume":"53","author":[{"given":"Long","family":"Sifan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8503-8061","authenticated-orcid":false,"given":"Wang","family":"Shengsheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhao","family":"Xin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fu","family":"Zihao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wang","family":"Bilin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"4262_CR1","unstructured":"Xu Y, Chen L, Duan L, Tsang IW, Luo J (2021) Open set domain adaptation with soft unknown-class rejection. IEEE Transactions on Neural Networks and Learning Systems"},{"key":"4262_CR2","doi-asserted-by":"crossref","unstructured":"Shermin T, Lu G, Teng SW, Murshed M, Sohel F (2020) Adversarial network with multiple classifiers for open set domain adaptation. IEEE Transactions on Multimedia","DOI":"10.1109\/TMM.2020.3016126"},{"key":"4262_CR3","doi-asserted-by":"crossref","unstructured":"Zhong L, Fang Z, Liu F, Yuan B, Zhang G, Lu J (2021) Bridging the theoretical bound and deep algorithms for open set domain adaptation. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109\/TNNLS.2021.3119965"},{"key":"4262_CR4","doi-asserted-by":"crossref","unstructured":"Liu J, Jing M, Li J, Lu K, Shen HT (2021) Open set domain adaptation via joint alignment and category separation. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109\/TNNLS.2021.3134673"},{"key":"4262_CR5","doi-asserted-by":"crossref","unstructured":"Fu J, Wu X, Zhang S, Yan J (2019) Improved open set domain adaptation with backpropagation. In: 2019 IEEE international conference on image processing (ICIP), IEEE pp 2506\u20132510","DOI":"10.1109\/ICIP.2019.8803287"},{"key":"4262_CR6","doi-asserted-by":"crossref","unstructured":"Jing M, Li J, Zhu L, Ding Z, Lu K, Yang Y (2021) Balanced open set domain adaptation via centroid alignment. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 8013\u20138020","DOI":"10.1609\/aaai.v35i9.16977"},{"key":"4262_CR7","unstructured":"Long M, Cao Y, Wang J, Jordan M (2015) Learning transferable features with deep adaptation networks. In: International conference on machine learning, PMLR pp 97\u2013105"},{"key":"4262_CR8","doi-asserted-by":"crossref","unstructured":"Wang J, Chen Y, Hao S, Feng W, Shen Z (2017) Balanced distribution adaptation for transfer learning. In: 2017 IEEE international conference on data mining (ICDM), IEEE pp 1129\u20131134","DOI":"10.1109\/ICDM.2017.150"},{"key":"4262_CR9","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Advances in neural information processing systems, p 27"},{"key":"4262_CR10","unstructured":"Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: International conference on machine learning, PMLR pp 1180\u20131189"},{"key":"4262_CR11","unstructured":"Long M, Cao Z, Wang J, Jordan MI (2018) Conditional adversarial domain adaptation. Advances in neural information processing systems, p 31"},{"key":"4262_CR12","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":"4262_CR13","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.patrec.2020.06.003","volume":"136","author":"MR Loghmani","year":"2020","unstructured":"Loghmani MR, Vincze M, Tommasi T (2020) Positive-unlabeled learning for open set domain adaptation. Pattern Recogn Lett 136:198\u2013204","journal-title":"Pattern Recogn Lett"},{"key":"4262_CR14","doi-asserted-by":"crossref","unstructured":"Panareda Busto P, Gall J (2017) Open set domain adaptation. In: Proceedings of the IEEE International conference on computer vision, pp 754\u2013763","DOI":"10.1109\/ICCV.2017.88"},{"key":"4262_CR15","doi-asserted-by":"crossref","unstructured":"Saito K, Yamamoto S, Ushiku Y, Harada T (2018) Open set domain adaptation by backpropagation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 153\u2013168","DOI":"10.1007\/978-3-030-01228-1_10"},{"key":"4262_CR16","doi-asserted-by":"crossref","unstructured":"Liu H, Cao Z, Long M, Wang J, Yang Q (2019) Separate to adapt: Open set domain adaptation via progressive separation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2927\u20132936","DOI":"10.1109\/CVPR.2019.00304"},{"key":"4262_CR17","doi-asserted-by":"crossref","unstructured":"Bucci S, Loghmani MR, Tommasi T (2020) On the effectiveness of image rotation for open set domain adaptation. In: European conference on computer vision, pp 422\u2013438. Springer","DOI":"10.1007\/978-3-030-58517-4_25"},{"key":"4262_CR18","unstructured":"Wang Q, Meng F, Breckon TP (2021) Progressively select and reject pseudo-labelled samples for open-set domain adaptation. arXiv:2110.12635"},{"key":"4262_CR19","doi-asserted-by":"publisher","first-page":"108616","DOI":"10.1016\/j.patcog.2022.108616","volume":"127","author":"Y Gao","year":"2022","unstructured":"Gao Y, Chen P, Gao Y, Wang J, Pan Y, Ma AJ (2022) Hierarchical feature disentangling network for universal domain adaptation. Pattern Recogn 127:108616","journal-title":"Pattern Recogn"},{"key":"4262_CR20","unstructured":"Kirichenko P, Izmailov P, Wilson AG (2020) Why normalizing flows fail to detect out-of-distribution data. arXiv:2006.08545"},{"key":"4262_CR21","doi-asserted-by":"crossref","unstructured":"Yan X, Zhang H, Xu X, Hu X, Heng P-A (2021) Learning semantic context from normal samples for unsupervised anomaly detection. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 3110\u20133118","DOI":"10.1609\/aaai.v35i4.16420"},{"key":"4262_CR22","unstructured":"Sun Y, Guo C, Li Y (2021) React: out-of-distribution detection with rectified activations. Advances in Neural Information Processing Systems, p 34"},{"key":"4262_CR23","doi-asserted-by":"crossref","unstructured":"Zaeemzadeh A, Bisagno N, Sambugaro Z, Conci N, Rahnavard N, Shah M (2021) Out-of-distribution detection using union of 1-dimensional subspaces. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp 9452\u20139461","DOI":"10.1109\/CVPR46437.2021.00933"},{"key":"4262_CR24","unstructured":"Huang R, Geng A, Li Y (2021) On the importance of gradients for detecting distributional shifts in the wild. Advances in Neural Information Processing Systems, p 34"},{"key":"4262_CR25","unstructured":"Liang S, Li Y, Srikant R (2017) Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv:1706.02690"},{"key":"4262_CR26","first-page":"21464","volume":"33","author":"W Liu","year":"2020","unstructured":"Liu W, Wang X, Owens J, Li Y (2020) Energy-based out-of-distribution detection. Adv Neural Inf Process Syst 33:21464\u201321475","journal-title":"Adv Neural Inf Process Syst"},{"key":"4262_CR27","unstructured":"Wang H, Liu W, Bocchieri A, Li Y (2021) Can multi-label classification networks know what they don\u2019t know? Advances in Neural Information Processing Systems, p 34"},{"issue":"1","key":"4262_CR28","first-page":"100","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan JA, Wong MA (1979) Algorithm as 136: a k-means clustering algorithm. J R Stat Soc Series C (applied statistics) 28(1):100\u2013108","journal-title":"J R Stat Soc Series C (applied statistics)"},{"key":"4262_CR29","unstructured":"Lee D-h et al (2013) Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on challenges in representation learning, ICML, vol 3, p 896"},{"issue":"11","key":"4262_CR30","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"4262_CR31","unstructured":"Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng AY (2011) Reading digits in natural images with unsupervised feature learning"},{"issue":"5","key":"4262_CR32","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"},{"key":"4262_CR33","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, pp 213\u2013226. Springer","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"4262_CR34","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":"4262_CR35","unstructured":"Peng X, Usman B, Kaushik N, Hoffman J, Wang D, Saenko K (2017) Visda: The visual domain adaptation challenge. arXiv:1710.06924"},{"key":"4262_CR36","doi-asserted-by":"crossref","unstructured":"You K, Long M, Cao Z, Wang J, Jordan MI (2019) Universal domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2720\u20132729","DOI":"10.1109\/CVPR.2019.00283"},{"key":"4262_CR37","doi-asserted-by":"crossref","unstructured":"Jain LP, Scheirer WJ, Boult TE (2014) Multi-class open set recognition using probability of inclusion. In: European conference on computer vision, pp 393\u2013409. Springer","DOI":"10.1007\/978-3-319-10578-9_26"},{"key":"4262_CR38","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":"4262_CR39","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"4262_CR40","doi-asserted-by":"crossref","unstructured":"Smith LN, Topin N (2019) Super-convergence: very fast training of neural networks using large learning rates. Artificial intelligence and machine learning for multi-domain operations applications, vol 11006, International Society for Optics and Photonics p 1100612","DOI":"10.1117\/12.2520589"},{"key":"4262_CR41","unstructured":"Lian Q, Li W, Chen L, Duan L (2019) Known-class aware self-ensemble for open set domain adaptation. arXiv:1905.01068"},{"issue":"10","key":"4262_CR42","doi-asserted-by":"publisher","first-page":"4309","DOI":"10.1109\/TNNLS.2020.3017213","volume":"32","author":"Z Fang","year":"2020","unstructured":"Fang Z, Lu J, Liu F, Xuan J, Zhang G (2020) Open set domain adaptation: Theoretical bound and algorithm. IEEE Trans Neural Netw Learn Syst 32(10):4309\u20134322","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4262_CR43","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.neucom.2020.05.032","volume":"410","author":"Y Gao","year":"2020","unstructured":"Gao Y, Ma AJ, Gao Y, Wang J, Pan Y (2020) Adversarial open set domain adaptation via progressive selection of transferable target samples. Neurocomputing 410:174\u2013184","journal-title":"Neurocomputing"},{"key":"4262_CR44","unstructured":"Kundu JN, Venkat N, Revanur A, Babu RV et al (2020) Towards inheritable models for open-set domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12376\u201312385"},{"issue":"3","key":"4262_CR45","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/s10994-016-5610-8","volume":"106","author":"PR Mendes J\u00fanior","year":"2017","unstructured":"Mendes J\u00fanior PR, De Souza RM, Werneck RdO, Stein BV, Pazinato DV, de Almeida WR, Penatti OA, Torres RdS, Rocha A (2017) Nearest neighbors distance ratio open-set classifier. Mach Learn 106(3):359\u2013386","journal-title":"Mach Learn"},{"issue":"2","key":"4262_CR46","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":"4262_CR47","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":"4262_CR48","doi-asserted-by":"crossref","unstructured":"Zhang J, Li W, Ogunbona P (2017) Joint geometrical and statistical alignment for visual domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1859\u20131867","DOI":"10.1109\/CVPR.2017.547"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04262-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04262-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04262-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T07:07:42Z","timestamp":1688713662000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04262-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,9]]},"references-count":48,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["4262"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04262-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,2,9]]},"assertion":[{"value":"12 October 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}