{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T20:30:02Z","timestamp":1771705802909,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100008528","name":"Anyang Institute of Technology","doi-asserted-by":"publisher","award":["40076317"],"award-info":[{"award-number":["40076317"]}],"id":[{"id":"10.13039\/501100008528","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Key Technologies R & D Program of Henan Province","award":["212102210088"],"award-info":[{"award-number":["212102210088"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876139"],"award-info":[{"award-number":["61876139"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10489-023-04686-2","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T03:42:27Z","timestamp":1687837347000},"page":"22348-22362","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Regularized label relaxation-based stacked autoencoder for zero-shot learning"],"prefix":"10.1007","volume":"53","author":[{"given":"Jianqiang","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiutai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyan","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"key":"4686_CR1","doi-asserted-by":"crossref","unstructured":"H.\u00a0Touvron, M.\u00a0Cord, A.\u00a0Sablayrolles, G.\u00a0Synnaeve, H.\u00a0J\u00e9gou, Going deeper with image transformers, in: Proceedings of the IEEE International Conference on Computer Vision, 2021, pp. 32\u201342","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"4686_CR2","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.patrec.2020.07.042","volume":"141","author":"P Wang","year":"2021","unstructured":"Wang P, Fan E, Wang P (2021) Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recogn. Lett. 141:61\u201367","journal-title":"Pattern Recogn. Lett."},{"issue":"2","key":"4686_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3293318","volume":"10","author":"W Wei","year":"2019","unstructured":"Wei W, Zheng VW, Han Y, Miao C (2019) A survey of zero-shot learning: Settings, methods, and applications. ACM Trans. Intell. Syst. Technol. 10(2):1\u201337","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"4686_CR4","unstructured":"R.\u00a0Socher, M.\u00a0Ganjoo, C.\u00a0D. Manning, A.\u00a0Ng, Zero-shot learning through cross-modal transfer, in: Advances in Neural Information Processing Systems, 2013, pp. 935\u2013943"},{"key":"4686_CR5","unstructured":"A.\u00a0Frome, G.\u00a0S. Corrado, J.\u00a0Shlens, S.\u00a0Bengio, J.\u00a0Dean, M.\u00a0Ranzato, T.\u00a0Mikolov, Devise: a deep visual-semantic embedding model, in: Advances in Neural Information Processing Systems, 2013, pp. 2121\u20132129"},{"key":"4686_CR6","doi-asserted-by":"crossref","unstructured":"J.\u00a0Li, M.\u00a0Jing, K.\u00a0Lu, Z.\u00a0Ding, L.\u00a0Zhu, Z.\u00a0Huang, Leveraging the invariant side of generative zero-shot learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 7402\u20137411","DOI":"10.1109\/CVPR.2019.00758"},{"key":"4686_CR7","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/j.neunet.2021.07.031","volume":"143","author":"R Zhang","year":"2021","unstructured":"Zhang R, Zhu Q, Xu X, Zhang D, Huang S-J (2021) Visual-guided attentive attributes embedding for zero-shot learning. Neural Networks 143:709\u2013718","journal-title":"Neural Networks"},{"key":"4686_CR8","doi-asserted-by":"crossref","unstructured":"E.\u00a0Kodirov, T.\u00a0Xiang, S.\u00a0Gong, Semantic autoencoder for zero-shot learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4447\u20134456","DOI":"10.1109\/CVPR.2017.473"},{"key":"4686_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2019.08.023","volume":"121","author":"Y Liu","year":"2020","unstructured":"Liu Y, Gao X, Gao Q, Han J, Shao L (2020) Label-activating framework for zero-shot learning. Neural Networks 121:1\u20139","journal-title":"Neural Networks"},{"key":"4686_CR10","doi-asserted-by":"crossref","unstructured":"Ji Z, Wang J, Yu Y, Pang Y, Han J (2019) Class-specific synthesized dictionary model for zero-shot learning. Neurocomputing 329:339\u2013347","DOI":"10.1016\/j.neucom.2018.10.069"},{"key":"4686_CR11","doi-asserted-by":"crossref","unstructured":"M.\u00a0Bucher, S.\u00a0Herbin, F.\u00a0Jurie, Improving semantic embedding consistency by metric learning for zero-shot classiffication, in: Proceedings of the European Conference on Computer Vision, 2016, pp. 730\u2013746","DOI":"10.1007\/978-3-319-46454-1_44"},{"key":"4686_CR12","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.neucom.2019.11.011","volume":"381","author":"C Pan","year":"2020","unstructured":"Pan C, Huang J, Hao J, Gong J (2020) Towards zero-shot learning generalization via a cosine distance loss. Neurocomputing 381:167\u2013176","journal-title":"Neurocomputing"},{"key":"4686_CR13","doi-asserted-by":"crossref","unstructured":"A.\u00a0Mishra, S.\u00a0Krishna\u00a0Reddy, A.\u00a0Mittal, H.\u00a0A. Murthy, A generative model for zero shot learning using conditional variational autoencoders, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 2188\u20132196","DOI":"10.1109\/CVPRW.2018.00294"},{"key":"4686_CR14","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Xian, T.\u00a0Lorenz, B.\u00a0Schiele, Z.\u00a0Akata, Feature generating networks for zero-shot learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 5542\u20135551","DOI":"10.1109\/CVPR.2018.00581"},{"key":"4686_CR15","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.neucom.2019.08.111","volume":"406","author":"Y Ma","year":"2020","unstructured":"Ma Y, Xu X, Shen F, Shen HT (2020) Similarity preserving feature generating networks for zero-shot learning. Neurocomputing 406:333\u2013342","journal-title":"Neurocomputing"},{"key":"4686_CR16","doi-asserted-by":"crossref","unstructured":"W.\u00a0Wang, Y.\u00a0Pu, V.\u00a0K. Verma, K.\u00a0Fan, Y.\u00a0Zhang, C.\u00a0Chen, P.\u00a0Rai, L.\u00a0Carin, Zero-shot learning via class-conditioned deep generative models, in: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018, pp. 4211\u20134218","DOI":"10.1609\/aaai.v32i1.11600"},{"key":"4686_CR17","doi-asserted-by":"crossref","unstructured":"M.\u00a0Ye, Y.\u00a0Guo, Progressive ensemble networks for zero-shot recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 11728\u201311736","DOI":"10.1109\/CVPR.2019.01200"},{"key":"4686_CR18","doi-asserted-by":"crossref","unstructured":"J.\u00a0Song, C.\u00a0Shen, Y.\u00a0Yang, Y.\u00a0Liu, M.\u00a0Song, Transductive unbiased embedding for zero-shot learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1024\u20131033","DOI":"10.1109\/CVPR.2018.00113"},{"key":"4686_CR19","doi-asserted-by":"crossref","unstructured":"S.\u00a0Changpinyo, W.-L. Chao, B.\u00a0Gong, F.\u00a0Sha, Synthesized classifiers for zero-shot learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 5327\u20135336","DOI":"10.1109\/CVPR.2016.575"},{"issue":"7","key":"4686_CR20","doi-asserted-by":"publisher","first-page":"2510","DOI":"10.1109\/TPAMI.2020.2965534","volume":"43","author":"J Guan","year":"2021","unstructured":"Guan J, Lu Z, Xiang T, Li A, Zhao A, Wen J-R (2021) Zero and few shot learning with semantic feature synthesis and competitive learning. IEEE Trans. Pattern Anal. Mach. Intell. 43(7):2510\u20132523","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4686_CR21","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Liu, Q.\u00a0Gao, J.\u00a0Han, S.\u00a0Wang, X.\u00a0Gao, Graph and autoencoder based feature extraction for zero-shot learning, in: Proceedings of the International Joint Conference on Artificial Intelligence, 2019, pp. 15\u201336","DOI":"10.24963\/ijcai.2019\/421"},{"key":"4686_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106773","volume":"215","author":"H Wu","year":"2021","unstructured":"Wu H, Yan Y, Chen S, Huang X, Wu Q, Ng MK (2021) Joint visual and semantic optimization for zero-shot learning. Knowl. Based Syst. 215:106773","journal-title":"Knowl. Based Syst."},{"key":"4686_CR23","unstructured":"B.\u00a0Romera-Paredes, P.\u00a0Torr, An embarrassingly simple approach to zero-shot learning, in: Proceedings of the International Conference on Machine Learning, 2015, pp. 2152\u20132161"},{"key":"4686_CR24","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Akata, S.\u00a0Reed, D.\u00a0Walter, H.\u00a0Lee, B.\u00a0Schiele, Evaluation of output embeddings for fine-grained image classification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2927\u20132936","DOI":"10.1109\/CVPR.2015.7298911"},{"issue":"9","key":"4686_CR25","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","volume":"41","author":"Y Xian","year":"2019","unstructured":"Xian Y, Schiele B, Akata Z (2019) Zero-shot learning-the good, the bad and the ugly. IEEE Trans. Pattern Anal. Mach. Intell. 41(9):2251\u20132265","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4686_CR26","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1109\/TMM.2020.2984091","volume":"23","author":"J Guo","year":"2021","unstructured":"Guo J, Guo S (2021) A novel perspective to zero-shot learning: Towards an alignment of manifold structures via semantic feature expansion. IEEE Trans. Multim. 23:524\u2013537","journal-title":"IEEE Trans. Multim."},{"key":"4686_CR27","doi-asserted-by":"crossref","unstructured":"L.\u00a0Zhang, T.\u00a0Xiang, S.\u00a0Gong, et\u00a0al., Learning a deep embedding model for zero-shot learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 3010\u20133019","DOI":"10.1109\/CVPR.2017.321"},{"key":"4686_CR28","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.neucom.2019.11.011","volume":"381","author":"C Pan","year":"2020","unstructured":"Pan C, Huang J, Hao J, Gong J (2020) Towards zero-shot learning generalization via a cosine distance loss. Neurocomputing 381:167\u2013176","journal-title":"Neurocomputing"},{"issue":"7","key":"4686_CR29","doi-asserted-by":"publisher","first-page":"3662","DOI":"10.1109\/TIP.2019.2899987","volume":"28","author":"F Shen","year":"2019","unstructured":"Shen F, Zhou X, Yu J, Yang Y, Liu L, Shen HT (2019) Scalable zero-shot learning via binary visual-semantic embeddings. IEEE Trans. Image Process. 28(7):3662\u20133674","journal-title":"IEEE Trans. Image Process."},{"key":"4686_CR30","doi-asserted-by":"publisher","first-page":"3665","DOI":"10.1109\/TIP.2020.2964429","volume":"29","author":"R Gao","year":"2020","unstructured":"Gao R, Hou X, Qin J, Chen J, Liu L, Zhu F, Zhang Z, Shao L (2020) Zero-vae-gan: Generating unseen features for generalized and transductive zero-shot learning. IEEE Trans. Image Process. 29:3665\u20133680","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"4686_CR31","doi-asserted-by":"publisher","first-page":"1738","DOI":"10.1109\/TNNLS.2012.2212721","volume":"23","author":"S Xiang","year":"2012","unstructured":"Xiang S, Nie F, Meng G, Pan C, Zhang C (2012) Discriminative least squares regression for multiclass classification and feature selection. IEEE Trans. Neural Netw. Learn. Syst. 23(11):1738\u20131754","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"2","key":"4686_CR32","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1109\/TCSVT.2018.2890511","volume":"30","author":"N Han","year":"2020","unstructured":"Han N, Wu J, Fang X, Wong WK, Xu Y, Yang J, Li X (2020) Double relaxed regression for image classification. IEEE Trans. Circuits Syst. Video Technol. 30(2):307\u2013319","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"1","key":"4686_CR33","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/s10489-021-02258-w","volume":"52","author":"J Ma","year":"2022","unstructured":"Ma J, Zhou S (2022) Discriminative least squares regression for multiclass classification based on within-class scatter minimization. Appl. Intell. 52(1):622\u2013635","journal-title":"Appl. Intell."},{"key":"4686_CR34","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1016\/j.neucom.2022.05.119","volume":"500","author":"H Han","year":"2022","unstructured":"Han H, Li W, Wang J, Qin G, Qin X (2022) Enhance explainability of manifold learning. Neurocomputing 500:877\u2013895","journal-title":"Neurocomputing"},{"issue":"9","key":"4686_CR35","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1145\/361573.361582","volume":"15","author":"RH Bartels","year":"1972","unstructured":"Bartels RH, Stewart GW (1972) Solution of the matrix equation ax+xb=c [f4]. Commun. ACM 15(9):820\u2013826","journal-title":"Commun. ACM"},{"key":"4686_CR36","doi-asserted-by":"crossref","unstructured":"J.\u00a0Song, G.\u00a0Shi, X.\u00a0Xie, D.\u00a0Gao, Zero-shot learning using stacked autoencoder with manifold regularizations, in: Proceedings of the IEEE International Conference on Image Processing, 2019, pp. 3651\u20133655","DOI":"10.1109\/ICIP.2019.8803509"},{"issue":"12","key":"4686_CR37","doi-asserted-by":"publisher","first-page":"9756","DOI":"10.1109\/TPAMI.2021.3132503","volume":"44","author":"X Luo","year":"2022","unstructured":"Luo X, Wu H, Wang Z, Wang J, Meng D (2022) A novel approach to large-scale dynamically weighted directed network representation. IEEE Trans. Pattern Anal. Mach. Intell. 44(12):9756\u20139773","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"4686_CR38","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1287\/moor.2017.0875","volume":"43","author":"D Han","year":"2018","unstructured":"Han D, Sun D, Zhang L (2018) Linear rate convergence of the alternating direction method of multipliers for convex composite programming. Math. Oper. Res. 43(2):622\u2013637","journal-title":"Math. Oper. Res."},{"key":"4686_CR39","doi-asserted-by":"crossref","unstructured":"A.\u00a0Farhadi, I.\u00a0Endres, D.\u00a0Hoiem, D.\u00a0Forsyth, Describing objects by their attributes, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1778\u20131785","DOI":"10.1109\/CVPR.2009.5206772"},{"key":"4686_CR40","unstructured":"C.\u00a0Wah, S.\u00a0Branson, P.\u00a0Welinder, P.\u00a0Perona, S.\u00a0Belongie, The caltech-ucsd birds-200-2011 dataset, Tech. rep. (2011)"},{"key":"4686_CR41","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s11263-013-0695-z","volume":"108","author":"G Patterson","year":"2014","unstructured":"Patterson G, Xu C, Su H, Hays J (2014) The sun attribute database: Beyond categories for deeper scene understanding. International Journal of Computer Vision 108:59\u201381","journal-title":"International Journal of Computer Vision"},{"issue":"3","key":"4686_CR42","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1109\/TCSVT.2022.3209209","volume":"33","author":"H Yang","year":"2023","unstructured":"Yang H, Sun B, Li B, Yang C, Wang Z, Chen J, Wang L, Li H (2023) Iterative class prototype calibration for transductive zero-shot learning. IEEE Trans. Circuits Syst. Video Technol. 33(3):1236\u20131246","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4686_CR43","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.patrec.2017.09.030","volume":"109","author":"T Long","year":"2018","unstructured":"Long T, Xu X, Shen F, Liu L, Xie N, Yang Y (2018) Zero-shot learning via discriminative representation extraction. Pattern Recogn. Lett. 109:27-34","journal-title":"Pattern Recogn. Lett."},{"key":"4686_CR44","doi-asserted-by":"crossref","unstructured":"V.\u00a0K. Verma, G.\u00a0Arora, A.\u00a0Mishra, P.\u00a0Rai, Generalized zero-shot learning via synthesized examples, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4281\u20134289","DOI":"10.1109\/CVPR.2018.00450"},{"issue":"11","key":"4686_CR45","doi-asserted-by":"publisher","first-page":"5652","DOI":"10.1109\/TIP.2018.2861573","volume":"27","author":"S Rahman","year":"2018","unstructured":"Rahman S, Khan S, Porikli F (2018) A unified approach for conventional zero-shot, generalized zero-shot, and few-shot learning. IEEE Trans. Image Process. 27(11):5652\u20135667","journal-title":"IEEE Trans. Image Process."},{"issue":"10","key":"4686_CR46","first-page":"2908","volume":"48","author":"Y Yu","year":"2018","unstructured":"Yu Y, Zhong J, Li X, Guo J, Zhang Z, Ling H, Wu F (2018) Transductive zero-shot learning with a self-training dictionary approach, IEEE Trans. Syst. Man. Cybern. B Cybern. 48(10):2908\u20132919","journal-title":"Syst. Man. Cybern. B Cybern."},{"key":"4686_CR47","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Guo, G.\u00a0Ding, X.\u00a0Jin, J.\u00a0Wang, Transductive zero-shot recognition via shared model space learning, in: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016, pp. 3494\u20133500","DOI":"10.1609\/aaai.v30i1.10448"},{"issue":"9","key":"4686_CR48","doi-asserted-by":"publisher","first-page":"4116","DOI":"10.1109\/TNNLS.2017.2753852","volume":"29","author":"Y Yu","year":"2018","unstructured":"Yu Y, Ji Z, Guo J, Pang Y (2018) Transductive zero-shot learning with adaptive structural embedding. IEEE Trans. Neural Netw. Learn. Syst. 29(9):4116\u20134127","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"4686_CR49","doi-asserted-by":"crossref","unstructured":"V.\u00a0K. Verma, P.\u00a0Rai, A simple exponential family framework for zero-shot learning, in: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017, pp. 792\u2013808","DOI":"10.1007\/978-3-319-71246-8_48"},{"key":"4686_CR50","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.neucom.2018.08.014","volume":"316","author":"Z Ji","year":"2018","unstructured":"Ji Z, Sun Y, Yu Y, Guo J, Pang Y (2018) Semantic softmax loss for zero-shot learning. Neurocomputing 316:369\u2013375","journal-title":"Neurocomputing"},{"issue":"10","key":"4686_CR51","doi-asserted-by":"publisher","first-page":"3755","DOI":"10.1109\/TCYB.2018.2850750","volume":"49","author":"Y Yu","year":"2019","unstructured":"Yu Y, Ji Z, Guo J, Zhang Z (2019) Zero-shot learning via latent space encoding. IEEE Trans. Cybern. 49(10):3755\u20133766","journal-title":"IEEE Trans. Cybern."},{"key":"4686_CR52","doi-asserted-by":"crossref","unstructured":"E.\u00a0Kodirov, T.\u00a0Xiang, Z.\u00a0Fu, S.\u00a0Gong, Unsupervised domain adaptation for zero-shot learning, in: Proceedings of the IEEE Conference on Computer Vision, 2015, pp. 2452\u20132460","DOI":"10.1109\/ICCV.2015.282"},{"issue":"3","key":"4686_CR53","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1109\/TPAMI.2013.140","volume":"36","author":"CH Lampert","year":"2014","unstructured":"Lampert CH, Nickisch H, Harmeling S (2014) Attribute-based classification for zero-shot visual object categorization. IEEE Trans. Pattern Anal. Mach. Intell. 36(3):453\u2013465","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4686_CR54","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Zhu, M.\u00a0Elhoseiny, B.\u00a0Liu, X.\u00a0Peng, A.\u00a0Elgammal, A generative adversarial approach for zero-shot learning from noisy texts, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1004\u20131013","DOI":"10.1109\/CVPR.2018.00111"},{"key":"4686_CR55","unstructured":"L. v. d. Maaten, G. Hinton, Visualizing data using t-sne, Journal of Machine Learning Research 9 (11) (2008) 2579-2605"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04686-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04686-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04686-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T13:21:37Z","timestamp":1697635297000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04686-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,27]]},"references-count":55,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["4686"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04686-2","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,27]]},"assertion":[{"value":"4 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they do have not any pertinent or potential conflicts which could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}