{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:49:04Z","timestamp":1763664544211,"version":"3.37.3"},"reference-count":87,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1747751"],"award-info":[{"award-number":["1747751"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s42979-021-00648-y","type":"journal-article","created":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T12:08:32Z","timestamp":1622549312000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Class Representative Learning for Zero-shot Learning Using Purely Visual Data"],"prefix":"10.1007","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3697-5972","authenticated-orcid":false,"given":"Mayanka","family":"Chandrashekar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yugyung","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,1]]},"reference":[{"issue":"2","key":"648_CR1","first-page":"13","volume":"10","author":"W Wang","year":"2019","unstructured":"Wang W, Zheng VW, Yu H, Miao C. A survey of zero-shot learning: Settings, methods, and applications. ACM Trans Intell Syst Technol. 2019;10(2):13.","journal-title":"ACM Trans Intell Syst Technol."},{"key":"648_CR2","unstructured":"Lake B, Salakhutdinov R, Gross J, Tenenbaum J. One shot learning of simple visual concepts. In: Proceedings of the Annual Meeting of the Cognitive Science Society; 2011. p. 33."},{"key":"648_CR3","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D, et\u00a0al. Matching networks for one shot learning. In: Advances in neural information processing systems; 2016. pp. 3630\u20138."},{"issue":"3","key":"648_CR4","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1109\/TPAMI.2013.140","volume":"36","author":"CH Lampert","year":"2013","unstructured":"Lampert CH, Nickisch H, Harmeling S. Attribute-based classification for zero-shot visual object categorization. IEEE Trans Pattern Anal Mach Intell. 2013;36(3):453\u201365.","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"issue":"7","key":"648_CR5","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1109\/TPAMI.2015.2487986","volume":"38","author":"Z Akata","year":"2015","unstructured":"Akata Z, Perronnin F, Harchaoui Z, Schmid C. Label-embedding for image classification. IEEE Trans Pattern Anal Mach Intell. 2015;38(7):1425\u201338.","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"648_CR6","unstructured":"Norouzi M, Mikolov T, Bengio S, Singer Y, Shlens J, Frome A, Corrado GS, Dean J. Zero-shot learning by convex combination of semantic embeddings. 2013. arXiv: 1312.5650."},{"key":"648_CR7","doi-asserted-by":"crossref","unstructured":"Xian Y, Schiele B, Akata Z. Zero-shot learning-the good, the bad and the ugly. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017. pp. 4582\u201391.","DOI":"10.1109\/CVPR.2017.328"},{"issue":"9","key":"648_CR8","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","volume":"41","author":"Y Xian","year":"2018","unstructured":"Xian Y, Lampert CH, Schiele B, Akata Z. Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans Pattern Anal Mach Intell. 2018;41(9):2251\u201365.","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"648_CR9","unstructured":"Kingma DP, Mohamed S, Rezende DJ, Welling M. Semi-supervised learning with deep generative models. In: Advances in neural information processing systems; 2014. pp. 3581\u20139."},{"key":"648_CR10","unstructured":"Diederik PK, Welling M, et\u00a0al. Auto-encoding variational bayes. In: Proceedings of the International Conference on Learning Representations (ICLR); 2014."},{"key":"648_CR11","unstructured":"Rezende DJ, Mohamed S, Danihelka I, Gregor K, Wierstra D. One-shot generalization in deep generative models. 2016. arXiv: 1603.05106."},{"key":"648_CR12","unstructured":"Xu X, Tsang IW, Liu C. Complementary attributes: a new clue to zero-shot learning. IEEE Trans Cybern. 2019."},{"key":"648_CR13","doi-asserted-by":"crossref","unstructured":"Akata Z, Malinowski M, Fritz M, Schiele B. Multi-cue zero-shot learning with strong supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016. pp. 59\u201368.","DOI":"10.1109\/CVPR.2016.14"},{"issue":"3","key":"648_CR14","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, Berg AC, Fei-Fei L. ImageNet large scale visual recognition challenge. Int J Comput Vision. 2015;115(3):211\u201352.","journal-title":"Int J Comput Vision."},{"key":"648_CR15","doi-asserted-by":"crossref","unstructured":"Kokkinos I. Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. pp. 6129\u201338.","DOI":"10.1109\/CVPR.2017.579"},{"key":"648_CR16","unstructured":"Rebuffi S-A, Bilen H, Vedaldi A. Learning multiple visual domains with residual adapters. In: Advances in neural information processing systems; 2017. pp. 506\u201316."},{"key":"648_CR17","unstructured":"Bilen H, Vedaldi, A. Universal representations: the missing link between faces, text, planktons, and cat breeds. 2017. arXiv: 1701.07275, 2017."},{"key":"648_CR18","doi-asserted-by":"crossref","unstructured":"Rebuffi S-A, Bilen H, Vedaldi A. Efficient parametrization of multi-domain deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2018. pp. 8119\u201327.","DOI":"10.1109\/CVPR.2018.00847"},{"key":"648_CR19","unstructured":"Kapoor A, Lee B, Tan D, Horvitz E. Learning to learn: Algorithmic inspirations from human problem solving. In: Twenty-Sixth AAAI Conference on Artificial Intelligence; 2012."},{"issue":"10","key":"648_CR20","doi-asserted-by":"publisher","first-page":"3109","DOI":"10.1109\/TIP.2015.2438548","volume":"24","author":"L Shen","year":"2015","unstructured":"Shen L, Sun G, Huang Q, Wang S, Lin Z, Wu E. Multi-level discriminative dictionary learning with application to large scale image classification. IEEE Trans Image Process. 2015;24(10):3109\u201323.","journal-title":"IEEE Trans Image Process."},{"key":"648_CR21","doi-asserted-by":"crossref","unstructured":"Changpinyo S, Chao W-L, Sha F. Predicting visual exemplars of unseen classes for zero-shot learning. In: Proceedings of the IEEE international conference on computer vision; 2017. pp. 3476\u201385.","DOI":"10.1109\/ICCV.2017.376"},{"key":"648_CR22","doi-asserted-by":"crossref","unstructured":"Zhang X, Gui S, Zhu Z, Zhao Y, Liu J. Hierarchical prototype learning for zero-shot recognition. IEEE Trans Multimedia. 2019.","DOI":"10.1109\/TMM.2019.2959433"},{"issue":"8","key":"648_CR23","doi-asserted-by":"publisher","first-page":"2009","DOI":"10.1109\/TPAMI.2017.2737007","volume":"40","author":"Z Fu","year":"2017","unstructured":"Fu Z, Xiang T, Kodirov E, Gong S. Zero-shot learning on semantic class prototype graph. IEEE Trans Pattern Anal Mach Intell. 2017;40(8):2009\u201322.","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"648_CR24","unstructured":"Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning. In: Advances in neural information processing systems; 2017. pp. 4077\u201387."},{"key":"648_CR25","unstructured":"Palatucci M, Pomerleau D, Hinton GE, Mitchell, TM. Zero-shot learning with semantic output codes. In: Advances in neural information processing systems; 2009. pp. 1410\u201318."},{"key":"648_CR26","doi-asserted-by":"crossref","unstructured":"Wolf L, Martin, I. Robust boosting for learning from few examples. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905). IEEE; 2005. pp. 1:359\u201364.","DOI":"10.1109\/CVPR.2005.305"},{"key":"648_CR27","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1109\/TPAMI.2007.1055","volume":"5","author":"A Torralba","year":"2007","unstructured":"Torralba A, Murphy KP, Freeman WT. Sharing visual features for multiclass and multiview object detection. IEEE Trans Pattern Anal Mach Intell. 2007;5:854\u201369.","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"648_CR28","unstructured":"Fleuret F, Blanchard, G. Pattern recognition from one example by chopping. In: Advances in neural information processing systems; 2006. pp. 371\u201378."},{"key":"648_CR29","doi-asserted-by":"crossref","unstructured":"Bart E, Ullman S, Cross-generalization: Learning novel classes from a single example by feature replacement. In: IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905), vol. 1. IEEE;2005. pp. 672\u20139.","DOI":"10.1109\/CVPR.2005.117"},{"key":"648_CR30","doi-asserted-by":"crossref","unstructured":"Amit Y, Fink M, Srebro N, Ullman, S. Uncovering shared structures in multiclass classification. In: Proceedings of the 24th international conference on Machine learning. ACM; 2007. pp.\u00a017\u201324.","DOI":"10.1145\/1273496.1273499"},{"key":"648_CR31","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems; 2013. pp. 3111\u201319."},{"key":"648_CR32","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C. Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014. pp. 1532\u201343.","DOI":"10.3115\/v1\/D14-1162"},{"key":"648_CR33","doi-asserted-by":"crossref","unstructured":"Akata Z, Reed S, Walter D, Lee H, Schiele B. Evaluation of output embeddings for fine-grained image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015. pp. 2927\u201336.","DOI":"10.1109\/CVPR.2015.7298911"},{"key":"648_CR34","unstructured":"Frome A, Corrado GS, Shlens J, Bengio S, Dean J, Ranzato M, Mikolov T. Devise: a deep visual-semantic embedding model. In: Advances in neural information processing systems; 2013. pp. 2121\u20139."},{"key":"648_CR35","doi-asserted-by":"crossref","unstructured":"Fu Z, Xiang T, Kodirov E, Gong S. Zero-shot object recognition by semantic manifold distance. In: The IEEE conference on computer vision and pattern recognition (CVPR). 2015.","DOI":"10.1109\/CVPR.2015.7298879"},{"key":"648_CR36","unstructured":"Romera-Paredes B, Torr P. An embarrassingly simple approach to zero-shot learning. In: International conference on machine learning; 2015. pp. 2152\u201361."},{"key":"648_CR37","doi-asserted-by":"crossref","unstructured":"Xian Y, Akata Z, Sharma G, Nguyen Q, Hein M, Schiele B. Latent embeddings for zero-shot classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. pp. 69\u201377.","DOI":"10.1109\/CVPR.2016.15"},{"key":"648_CR38","unstructured":"Yu Y, Zhang Z, Han J. Meta-transfer networks for zero-shot learning. 2019. arXiv:1909.03360."},{"key":"648_CR39","doi-asserted-by":"crossref","unstructured":"Wang Q, Chen K. Alternative semantic representations for zero-shot human action recognition. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer; 2017. pp. 87\u2013102.","DOI":"10.1007\/978-3-319-71249-9_6"},{"key":"648_CR40","doi-asserted-by":"crossref","unstructured":"Wang Q, Bu P, Breckon TP. Unifying unsupervised domain adaptation and zero-shot visual recognition. 2019. arXiv:1903.10601.","DOI":"10.1109\/IJCNN.2019.8852015"},{"key":"648_CR41","doi-asserted-by":"crossref","unstructured":"Zhu Y, Long Y, Guan Y, Newsam S, Shao L. Towards universal representation for unseen action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2018. pp. 9436\u201345.","DOI":"10.1109\/CVPR.2018.00983"},{"key":"648_CR42","doi-asserted-by":"crossref","unstructured":"Fu Y, Wang X, Dong H, Jiang Y-G, Wang M, Xue X, Sigal L. Vocabulary-informed zero-shot and open-set learning. IEEE Trans Pattern Anal Mach Intell. 2019.","DOI":"10.1109\/TPAMI.2019.2922175"},{"key":"648_CR43","doi-asserted-by":"crossref","unstructured":"Farhadi A, Endres I, Hoiem D, Forsyth D, Describing objects by their attributes. In: IEEE conference on computer vision and pattern recognition. IEEE; 2009. pp. 1778\u201385.","DOI":"10.1109\/CVPRW.2009.5206772"},{"key":"648_CR44","unstructured":"Socher R, Ganjoo M, Manning CD, Ng A. Zero-shot learning through cross-modal transfer. In: Advances in neural information processing systems. 2013; pp.\u00a0935\u201343."},{"key":"648_CR45","doi-asserted-by":"crossref","unstructured":"Kodirov E, Xiang T, Gong S. Semantic autoencoder for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. pp. 3174\u201383.","DOI":"10.1109\/CVPR.2017.473"},{"key":"648_CR46","doi-asserted-by":"crossref","unstructured":"Zhang L, Xiang T, Gong S. Learning a deep embedding model for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. pp. 2021\u201330.","DOI":"10.1109\/CVPR.2017.321"},{"key":"648_CR47","doi-asserted-by":"crossref","unstructured":"Xian Y, Lorenz T, Schiele B, Akata Z. Feature generating networks for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2018. pp. 5542\u201351.","DOI":"10.1109\/CVPR.2018.00581"},{"key":"648_CR48","doi-asserted-by":"crossref","unstructured":"Schonfeld E, Ebrahimi S, Sinha S, Darrell T, Akata Z. Generalized zero-and few-shot learning via aligned variational autoencoders. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2019. pp. 8247\u201355.","DOI":"10.1109\/CVPR.2019.00844"},{"key":"648_CR49","doi-asserted-by":"crossref","unstructured":"Felix R, Kumar VB, Reid I, Carneiro G. Multi-modal cycle-consistent generalized zero-shot learning. In: Proceedings of the European Conference on Computer Vision (ECCV); 2018. pp. 21\u201337.","DOI":"10.1007\/978-3-030-01231-1_2"},{"key":"648_CR50","doi-asserted-by":"crossref","unstructured":"Guan J, Lu Z, Xiang T, Li A, Zhao A, Wen J-R. Zero and few shot learning with semantic feature synthesis and competitive learning. IEEE Trans Pattern Anal Mach Intell. 2020.","DOI":"10.1109\/TPAMI.2020.2965534"},{"key":"648_CR51","doi-asserted-by":"crossref","unstructured":"Zhang Z, Saligrama V. Zero-shot learning via semantic similarity embedding. In: Proceedings of the IEEE international conference on computer vision; 2015. pp. 4166\u201374.","DOI":"10.1109\/ICCV.2015.474"},{"key":"648_CR52","doi-asserted-by":"crossref","unstructured":"Changpinyo S, Chao W-L, Gong B, Sha F. Synthesized classifiers for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. pp. 5327\u201336.","DOI":"10.1109\/CVPR.2016.575"},{"key":"648_CR53","doi-asserted-by":"crossref","unstructured":"Chen L, Zhang H, Xiao J, Liu W, Chang S-F. Zero-shot visual recognition using semantics-preserving adversarial embedding networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2018. pp. 1043\u201352.","DOI":"10.1109\/CVPR.2018.00115"},{"key":"648_CR54","doi-asserted-by":"crossref","unstructured":"Akata Z, Perronnin F, Harchaoui Z, Schmid C. Label-embedding for attribute-based classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2013. pp. 819\u201326.","DOI":"10.1109\/CVPR.2013.111"},{"key":"648_CR55","doi-asserted-by":"crossref","unstructured":"Lei\u00a0Ba J, Swersky K, Fidler S, et\u00a0al. Predicting deep zero-shot convolutional neural networks using textual descriptions. In: Proceedings of the IEEE international conference on computer vision; 2015. pp. 4247\u201355.","DOI":"10.1109\/ICCV.2015.483"},{"key":"648_CR56","doi-asserted-by":"crossref","unstructured":"Yazdani M, Henderson J. A model of zero-shot learning of spoken language understanding. In: Proceedings of the 2015 conference on empirical methods in natural language processing; 2015. pp. 244\u20139.","DOI":"10.18653\/v1\/D15-1027"},{"key":"648_CR57","unstructured":"Mikolov T, Le QV, Sutskever I. Exploiting similarities among languages for machine translation. 2013. arXiv:1309.4168."},{"key":"648_CR58","doi-asserted-by":"crossref","unstructured":"Shigeto Y, Suzuki I, Hara K, Shimbo M, Matsumoto Y. \u201cRidge regression, hubness, and zero-shot learning. In: Joint European Conference on machine learning and knowledge discovery in databases. Springer; 2015. pp.\u00a0135\u201351.","DOI":"10.1007\/978-3-319-23528-8_9"},{"issue":"10","key":"648_CR59","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1109\/TPAMI.2017.2762295","volume":"40","author":"Y Long","year":"2017","unstructured":"Long Y, Liu L, Shen F, Shao L, Li X. Zero-shot learning using synthesised unseen visual data with diffusion regularisation. IEEE Trans Pattern Anal Mach Intell. 2017;40(10):2498\u2013512.","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"issue":"3","key":"648_CR60","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/s11263-016-0983-5","volume":"123","author":"X Xu","year":"2017","unstructured":"Xu X, Hospedales T, Gong S. Transductive zero-shot action recognition by word-vector embedding. Int J Comput Vision. 2017;123(3):309\u201333.","journal-title":"Int J Comput Vision."},{"key":"648_CR61","doi-asserted-by":"crossref","unstructured":"Kodirov E, Xiang T, Fu Z, Gong S. Unsupervised domain adaptation for zero-shot learning. In: Proceedings of the IEEE international conference on computer vision; 2015. pp. 2452\u201360.","DOI":"10.1109\/ICCV.2015.282"},{"issue":"2","key":"648_CR62","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1109\/TITS.2012.2217377","volume":"36","author":"Y Fu","year":"2013","unstructured":"Fu Y, Hospedales TM, Xiang T, Gong S. Learning multimodal latent attributes. IEEE Trans Pattern Anal Mach Intell. 2013;36(2):303\u201316.","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"648_CR63","doi-asserted-by":"crossref","unstructured":"Fu Y, Sigal L. Semi-supervised vocabulary-informed learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. pp. 5337\u201346.","DOI":"10.1109\/CVPR.2016.576"},{"issue":"3","key":"648_CR64","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1007\/s11263-017-1027-5","volume":"124","author":"Q Wang","year":"2017","unstructured":"Wang Q, Chen K. Zero-shot visual recognition via bidirectional latent embedding. Int J Comput Vision. 2017;124(3):356\u201383.","journal-title":"Int J Comput Vision."},{"key":"648_CR65","doi-asserted-by":"crossref","unstructured":"Xu B, Fu Y, Jiang Y-G, Li B, Sigal L. Video emotion recognition with transferred deep feature encodings. In: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval; 2016. pp. 15\u201322.","DOI":"10.1145\/2911996.2912006"},{"key":"648_CR66","doi-asserted-by":"crossref","unstructured":"Xu X, Shen F, Yang Y, Zhang D, Tao\u00a0Shen H, Song J. Matrix tri-factorization with manifold regularizations for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. pp. 3798\u201307.","DOI":"10.1109\/CVPR.2017.217"},{"key":"648_CR67","doi-asserted-by":"crossref","unstructured":"Tamaazousti Y, Le Borgne H, Hudelot C, Seddik MEA, Tamaazousti M. Learning more universal representations for transfer-learning. IEEE Trans Pattern Anal Mach Intell. 2019.","DOI":"10.1109\/TPAMI.2019.2913857"},{"key":"648_CR68","unstructured":"Chandrashekar M, Lee Y. Crl: Class representative learning for image classification. 2020. arXiv:2002.06619."},{"key":"648_CR69","doi-asserted-by":"crossref","unstructured":"Raina R, Battle A, Lee H, Packer B, Ng AY. Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th international conference on machine learning. ACM; 2007. pp.\u00a0759\u201366.","DOI":"10.1145\/1273496.1273592"},{"key":"648_CR70","doi-asserted-by":"crossref","unstructured":"Ben-David S, Blitzer J, Crammer K, Pereira F. Analysis of representations for domain adaptation. In: Advances in neural information processing systems; 2007. pp. 137\u201344.","DOI":"10.7551\/mitpress\/7503.003.0022"},{"key":"648_CR71","unstructured":"Blitzer J, Crammer K, Kulesza A, Pereira F, Wortman J. Learning bounds for domain adaptation. In: Advances in neural information processing systems; 2008. pp. 129\u201336."},{"key":"648_CR72","doi-asserted-by":"crossref","unstructured":"Kifer D, Ben-David S, Gehrke J. Detecting change in data streams. In: VLDB, vol.\u00a04. Toronto; 2004. pp.\u00a0180\u201391.","DOI":"10.1016\/B978-012088469-8.50019-X"},{"key":"648_CR73","unstructured":"Fei-Fei L, Fergus R, Perona P, Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In. Conference on computer vision and pattern recognition workshop. IEEE;2004. p. 178."},{"key":"648_CR74","unstructured":"Griffin G, Holub A, Perona P. Caltech-256 object category dataset. 2007."},{"key":"648_CR75","unstructured":"Welinder P, Branson S, Mita T, Wah C, Schroff F, Belongie S, Perona P. Caltech-UCSD Birds 200. Tech. Rep. CNS-TR-2010-001, California Institute of Technology. 2010."},{"issue":"1\u20132","key":"648_CR76","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. The sun attribute database: beyond categories for deeper scene understanding. Int J Comput Vision. 2014;108(1\u20132):59\u201381.","journal-title":"Int J Comput Vision."},{"key":"648_CR77","unstructured":"Pretrained inception-v3 convolutional neural network - matlab inceptionv3 - mathworks india. 2019. https:\/\/in.mathworks.com\/help\/deeplearning\/ref\/inceptionv3.html. (Accessed on 05\/13\/2019)."},{"key":"648_CR78","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. pp.\u00a02818\u201326.","DOI":"10.1109\/CVPR.2016.308"},{"key":"648_CR79","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"648_CR80","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. arXiv:1409.1556."},{"key":"648_CR81","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. pp. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"648_CR82","unstructured":"Foundation AS \u201cApache spark\u201d. 2019. Accessed on 26 May 2019."},{"issue":"1","key":"648_CR83","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s40537-017-0089-0","volume":"4","author":"O Day","year":"2017","unstructured":"Day O, Khoshgoftaar TM. A survey on heterogeneous transfer learning. J Big Data. 2017;4(1):29.","journal-title":"J Big Data."},{"key":"648_CR84","doi-asserted-by":"crossref","unstructured":"Kornblith S, Shlens J, Le QV. Do better imagenet models transfer better? 2018. arXiv:1805.08974.","DOI":"10.1109\/CVPR.2019.00277"},{"key":"648_CR85","doi-asserted-by":"crossref","unstructured":"Ulusoy I, Bishop CM. Comparison of generative and discriminative techniques for object detection and classification. In: Toward category-level object recognition. Springer; 2006. pp.\u00a0173\u201395.","DOI":"10.1007\/11957959_9"},{"key":"648_CR86","doi-asserted-by":"crossref","unstructured":"Wang W, Pu Y, Verma VK, Fan K, Zhang Y, Chen C, Rai P, Carin L. Zero-shot learning via class-conditioned deep generative models. In: Thirty-second AAAI conference on artificial intelligence. 2018.","DOI":"10.1609\/aaai.v32i1.11600"},{"key":"648_CR87","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1109\/TPAMI.2012.256","volume":"35","author":"WJ Scheirer","year":"2012","unstructured":"Scheirer WJ, de Rezende Rocha A, Sapkota A, Boult TE. Toward open set recognition. IEEE Trans Pattern Anal Mach Intell. 2012;35:1757\u201372.","journal-title":"IEEE Trans Pattern Anal Mach Intell."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00648-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-021-00648-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00648-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T23:32:39Z","timestamp":1725147159000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-021-00648-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,1]]},"references-count":87,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["648"],"URL":"https:\/\/doi.org\/10.1007\/s42979-021-00648-y","relation":{},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"type":"print","value":"2662-995X"},{"type":"electronic","value":"2661-8907"}],"subject":[],"published":{"date-parts":[[2021,6,1]]},"assertion":[{"value":"11 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2021","order":3,"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 states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"313"}}