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These classes can be referred to as<jats:italic>unseen classes<\/jats:italic>. Open-world Machine Learning (OWML) is a novel technique, which deals with unseen classes. Although OWML is around for a few years and many significant research works have been carried out in this domain, there is no comprehensive survey of the characteristics, applications, and impact of OWML on the major research areas. In this article, we aimed to capture the different dimensions of OWML with respect to other traditional machine learning models. We have thoroughly analyzed the existing literature and provided a novel taxonomy of OWML considering its two major application domains: Computer Vision and Natural Language Processing. We listed the available software packages and open datasets in OWML for future researchers. Finally, the article concludes with a set of research gaps, open challenges, and future directions.<\/jats:p>","DOI":"10.1145\/3561381","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T11:27:02Z","timestamp":1662550022000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":111,"title":["Open-world Machine Learning: Applications, Challenges, and Opportunities"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8290-7903","authenticated-orcid":false,"given":"Jitendra","family":"Parmar","sequence":"first","affiliation":[{"name":"Malaviya National Institute of Technology Jaipur, Rajasthan, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0280-7364","authenticated-orcid":false,"given":"Satyendra","family":"Chouhan","sequence":"additional","affiliation":[{"name":"Malaviya National Institute of Technology Jaipur, Rajasthan, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3722-6954","authenticated-orcid":false,"given":"Vaskar","family":"Raychoudhury","sequence":"additional","affiliation":[{"name":"Miami University, Oxford, Ohio, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2087-1666","authenticated-orcid":false,"given":"Santosh","family":"Rathore","sequence":"additional","affiliation":[{"name":"ABV-IIITM Gwalior, Gwalior, Madhya Pradesh, India"}]}],"member":"320","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"issue":"5","key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1007\/s12553-021-00579-x","article-title":"Development of IoT-based mhealth framework for various cases of heart disease patients","volume":"11","author":"Albahri A. 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Retrieved from https:\/\/arxiv.org\/abs\/1604.02275.","journal-title":"arXiv:1604.02275"},{"key":"e_1_3_3_24_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/978-3-030-67303-1_10","article-title":"Artificial intelligence for chatbots in mental health: Opportunities and challenges","author":"Denecke Kerstin","year":"2021","unstructured":"Kerstin Denecke, Alaa Abd-Alrazaq, and Mowafa Househ. 2021. Artificial intelligence for chatbots in mental health: Opportunities and challenges. In Multiple Perspectives on Artificial Intelligence in Healthcare. Springer, 115\u2013128.","journal-title":"Multiple Perspectives on Artificial Intelligence in Healthcare"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-015-0661-2"},{"key":"e_1_3_3_27_2","unstructured":"Jacob Devlin Ming-Wei Chang Kenton Lee and Kristina Toutanova. 2019. 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Generative OpenMax for multi-class open set classification. In Proceedings of the British Machine Vision Conference. British Machine Vision Association and Society for Pattern Recognition."},{"key":"e_1_3_3_35_2","article-title":"Recent advances in open set recognition: A survey","author":"Geng Chuanxing","year":"2020","unstructured":"Chuanxing Geng, Sheng-jun Huang, and Songcan Chen. 2020. Recent advances in open set recognition: A survey. IEEE Trans. Pattern Anal. Machine Intell. 43, 10 (2020), 3614\u20133631.","journal-title":"IEEE Trans. Pattern Anal. Machine Intell."},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000042993.50813.60"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.5555\/3086952"},{"key":"e_1_3_3_38_2","doi-asserted-by":"crossref","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial networks. 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Retrieved from https:\/\/arxiv.org\/abs\/1610.02136.","journal-title":"arXiv:1610.02136"},{"key":"e_1_3_3_49_2","first-page":"1074","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Hu Qianjiang","year":"2021","unstructured":"Qianjiang Hu, Xiao Wang, Wei Hu, and Guo-Jun Qi. 2021. Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1074\u20131083."},{"key":"e_1_3_3_50_2","article-title":"OSVidCap: A framework for the simultaneous recognition and description of concurrent actions in videos in an open-set scenario","author":"In\u00e1cio Andrei De Souza","year":"2021","unstructured":"Andrei De Souza In\u00e1cio, Matheus Gutoski, Andr\u00e9 Eug\u00eanio Lazzaretti, and Heitor Silv\u00e9rio Lopes. 2021. 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(2021), 1\u201326.","journal-title":"Wireless Pers. Commun."},{"key":"e_1_3_3_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741669"},{"key":"e_1_3_3_55_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-016-5610-8"},{"key":"e_1_3_3_56_2","article-title":"Switchboard SWBD-DAMSL Shallow-discourse-function Annotation Coders Manual","author":"Jurafsky Dan","year":"1997","unstructured":"Dan Jurafsky. 1997. Switchboard SWBD-DAMSL Shallow-discourse-function Annotation Coders Manual. Institute of Cognitive Science Technical Report.","journal-title":"Institute of Cognitive Science Technical Report"},{"key":"e_1_3_3_57_2","unstructured":"Mohammed Waleed Kadous. 2002. Temporal classification: Extending the classification paradigm to multivariate time series. University of New South Wales Kensington."},{"issue":"4","key":"e_1_3_3_58_2","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/TSMC.1985.6313426","article-title":"A fuzzy k-nearest neighbor algorithm","author":"Keller James M.","year":"1985","unstructured":"James M. Keller, Michael R. Gray, and James A. Givens. 1985. A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybernet.4 (1985), 580\u2013585.","journal-title":"IEEE Trans. Syst. Man Cybernet."},{"issue":"14","key":"e_1_3_3_59_2","first-page":"3384","article-title":"AI based facial recognition technology and criminal justice: Issues and challenges","volume":"12","author":"Khan Zubair Ahmed","year":"2021","unstructured":"Zubair Ahmed Khan and Asma Rizvi. 2021. AI based facial recognition technology and criminal justice: Issues and challenges. Turk. J. Comput. Math. Educ. 12, 14 (2021), 3384\u20133392.","journal-title":"Turk. J. Comput. Math. Educ."},{"key":"e_1_3_3_60_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_3_3_61_2","first-page":"813","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Kong Shu","year":"2021","unstructured":"Shu Kong and Deva Ramanan. 2021. Opengan: Open-set recognition via open data generation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 813\u2013822."},{"issue":"1","key":"e_1_3_3_62_2","first-page":"3","article-title":"Supervised machine learning: A review of classification techniques","volume":"160","author":"Kotsiantis Sotiris B.","year":"2007","unstructured":"Sotiris B. Kotsiantis, I. Zaharakis, P. Pintelas, et\u00a0al. 2007. Supervised machine learning: A review of classification techniques. Emerg. Artif. Intell. Appl. Comput. Eng. 160, 1 (2007), 3\u201324.","journal-title":"Emerg. Artif. Intell. Appl. Comput. Eng."},{"key":"e_1_3_3_63_2","unstructured":"Alex Krizhevsky Geoffrey Hinton et\u00a0al. 2009. Learning multiple layers of features from tiny images. Master\u2019s thesis. Department of Computer Science University of Toronto."},{"key":"e_1_3_3_64_2","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25. 1097\u20131105.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_65_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-7908-1850-5","volume-title":"Fuzzy Classifier Design","author":"Kuncheva Ludmila","year":"2000","unstructured":"Ludmila Kuncheva. 2000. Fuzzy Classifier Design. Vol. 49. Springer Science & Business Media."},{"key":"e_1_3_3_66_2","first-page":"331","volume-title":"Machine Learning Proceedings","author":"Lang Ken","year":"1995","unstructured":"Ken Lang. 1995. Newsweeder: Learning to filter netnews. In Machine Learning Proceedings, LakeTahoe, CA, 331\u2013339."},{"key":"e_1_3_3_67_2","first-page":"2169","volume-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2","author":"Lazebnik Svetlana","year":"2006","unstructured":"Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. 2006. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2. 2169\u20132178."},{"key":"e_1_3_3_68_2","unstructured":"Ya Le and Xuan Yang. 2015. Tiny imagenet visual recognition challenge. Stanford CS 231N Course. Retrieved from http:\/\/cs231n.stanford.edu\/reports\/2015\/pdfs\/yle_project.pdf."},{"key":"e_1_3_3_69_2","unstructured":"Yann LeCun. 1998. The MNIST Database of Handwritten Digits. Retrieved from http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"e_1_3_3_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_3_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.92"},{"key":"e_1_3_3_72_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2898940"},{"key":"e_1_3_3_73_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"e_1_3_3_74_2","first-page":"5491","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Lin Ting-En","year":"2019","unstructured":"Ting-En Lin and Hua Xu. 2019. Deep unknown intent detection with margin loss. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 5491\u20135496."},{"key":"e_1_3_3_75_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.104979"},{"issue":"5","key":"e_1_3_3_76_2","first-page":"986","article-title":"Determine the number of unknown targets in open world based on elbow method","volume":"29","author":"Liu Fan","year":"2020","unstructured":"Fan Liu and Yong Deng. 2020. Determine the number of unknown targets in open world based on elbow method. IEEE Trans. Fuzzy Syst. 29, 5 (2020), 986\u2013995.","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"e_1_3_3_77_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544013"},{"key":"e_1_3_3_78_2","first-page":"1012","volume-title":"Proceedings of the Future Technologies Conference","author":"Lokman Abbas Saliimi","year":"2018","unstructured":"Abbas Saliimi Lokman and Mohamed Ariff Ameedeen. 2018. Modern chatbot systems: A technical review. 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Retrieved from https:\/\/arxiv.org\/abs\/1802.06024.","journal-title":"arXiv:1802.06024"},{"key":"e_1_3_3_83_2","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_3_84_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.83"},{"key":"e_1_3_3_85_2","unstructured":"Donald Michie David J. Spiegelhalter and Charles C. Taylor. 1994. Machine learning neural and statistical classification. Retrieved from ftp.ncc.up.pt\/pub\/statlog\/ Data available at http:\/\/www.ncc.up.pt\/liacc\/ML."},{"key":"e_1_3_3_86_2","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv:1301.3781. Retrieved from https:\/\/arxiv.org\/abs\/1301.3781.","journal-title":"arXiv:1301.3781"},{"key":"e_1_3_3_87_2","first-page":"3111","volume-title":"Advances in Neural Information Processing Systems","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems. 3111\u20133119."},{"key":"e_1_3_3_88_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00361"},{"key":"e_1_3_3_89_2","doi-asserted-by":"publisher","DOI":"10.1145\/219717.219748"},{"key":"e_1_3_3_90_2","first-page":"387","volume-title":"Proceedings of the IEEE 29th International Conference on Advanced Information Networking and Applications Workshops","author":"Moore Philip","year":"2015","unstructured":"Philip Moore and Hai Van Pham. 2015. On context and the open world assumption. 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In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), Vol. 2. 2161\u20132168."},{"key":"e_1_3_3_99_2","first-page":"145","article-title":"An application for admission in public school systems","volume":"1","author":"Olave Manuel","year":"1989","unstructured":"Manuel Olave, Vladislav Rajkovic, and Marko Bohanec. 1989. An application for admission in public school systems. Expert Syst. Publ. Admin. 1 (1989), 145\u2013160.","journal-title":"Expert Syst. Publ. Admin."},{"key":"e_1_3_3_100_2","first-page":"2307","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Oza Poojan","year":"2019","unstructured":"Poojan Oza and Vishal M. Patel. 2019. C2ae: Class conditioned auto-encoder for open-set recognition. 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In Proceedings of the International Conference on Big Data Analytics. 329\u2013343."},{"key":"e_1_3_3_103_2","first-page":"1","article-title":"CyberBERT: BERT for cyberbullying identification","author":"Paul Sayanta","year":"2020","unstructured":"Sayanta Paul and Sriparna Saha. 2020. CyberBERT: BERT for cyberbullying identification. Multimedia Syst. (2020), 1\u20138.","journal-title":"Multimedia Syst."},{"key":"e_1_3_3_104_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_3_105_2","first-page":"61","article-title":"Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods","volume":"10","author":"Platt John","year":"1999","unstructured":"John Platt et\u00a0al. 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers, Vol. 10. 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First Monday (2014).","journal-title":"First Monday"},{"key":"e_1_3_3_108_2","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/978-3-319-13704-9_33","volume-title":"Proceedings of the International Conference on Knowledge Engineering and Knowledge Management","author":"Ratcliffe David","year":"2014","unstructured":"David Ratcliffe and Kerry Taylor. 2014. Closed-world concept induction for learning in OWL knowledge bases. In Proceedings of the International Conference on Knowledge Engineering and Knowledge Management. Springer, 429\u2013440."},{"key":"e_1_3_3_109_2","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/978-3-319-65981-7_12","article-title":"Deep learning for medical image processing: Overview, challenges and the future","author":"Razzak Muhammad Imran","year":"2018","unstructured":"Muhammad Imran Razzak, Saeeda Naz, and Ahmad Zaib. 2018. Deep learning for medical image processing: Overview, challenges and the future. Classif. 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Retrieved from http:\/\/qwone.com\/jason\/20Newsgroups.","journal-title":"Retrieved from http:\/\/qwone.com\/jason\/20Newsgroups"},{"key":"e_1_3_3_112_2","doi-asserted-by":"publisher","DOI":"10.5555\/1005332.1005336"},{"key":"e_1_3_3_113_2","first-page":"3654","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Ristin Marko","year":"2014","unstructured":"Marko Ristin, Matthieu Guillaumin, Juergen Gall, and Luc Van Gool. 2014. Incremental learning of ncm forests for large-scale image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3654\u20133661."},{"key":"e_1_3_3_114_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0020-0255(02)00369-9"},{"key":"e_1_3_3_115_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_3_116_2","doi-asserted-by":"publisher","DOI":"10.1145\/3446374"},{"issue":"7","key":"e_1_3_3_117_2","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1109\/TPAMI.2012.256","article-title":"Toward open set recognition","volume":"35","author":"Scheirer Walter J.","year":"2012","unstructured":"Walter J. Scheirer, Anderson de Rezende Rocha, Archana Sapkota, and Terrance E. Boult. 2012. Toward open set recognition. IEEE Trans. Pattern Anal. Mach. Intelligence 35, 7 (2012), 1757\u20131772.","journal-title":"IEEE Trans. Pattern Anal. Mach. 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Open-world knowledge graph completion. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32."},{"key":"e_1_3_3_124_2","doi-asserted-by":"publisher","DOI":"10.1177\/002383099804100410"},{"key":"e_1_3_3_125_2","first-page":"2911","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"Shu Lei","year":"2017","unstructured":"Lei Shu, Hu Xu, and Bing Liu. 2017. DOC: Deep open classification of text documents. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2911\u20132916."},{"key":"e_1_3_3_126_2","article-title":"Unseen class discovery in open-world classification","author":"Shu Lei","year":"2018","unstructured":"Lei Shu, Hu Xu, and Bing Liu. 2018. Unseen class discovery in open-world classification. arXiv:1801.05609. Retrieved from https:\/\/arxiv.org\/abs\/1801.05609.","journal-title":"arXiv:1801.05609"},{"key":"e_1_3_3_127_2","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. Retrieved from https:\/\/arxiv.org\/abs\/1409.1556.","journal-title":"arXiv:1409.1556"},{"key":"e_1_3_3_128_2","article-title":"A critical evaluation of open-world machine learning","author":"Song Liwei","year":"2020","unstructured":"Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji, and Prateek Mittal. 2020. A critical evaluation of open-world machine learning. arXiv:2007.04391. Retrieved from https:\/\/arxiv.org\/abs\/2007.04391.","journal-title":"arXiv:2007.04391"},{"key":"e_1_3_3_129_2","article-title":"Highway networks","author":"Srivastava Rupesh Kumar","year":"2015","unstructured":"Rupesh Kumar Srivastava, Klaus Greff, and J\u00fcrgen Schmidhuber. 2015. Highway networks. arXiv:1505.00387. Retrieved from https:\/\/arxiv.org\/abs\/1505.00387.","journal-title":"arXiv:1505.00387"},{"key":"e_1_3_3_130_2","first-page":"91","article-title":"Efficient uncertainty estimation for open-set object detection","author":"Stevens Wallace","year":"2021","unstructured":"Wallace Stevens. 2021. Efficient uncertainty estimation for open-set object detection. In Epistemic Uncertainty Estimation for Object Detection in Open-Set Conditions, 91.","journal-title":"Epistemic Uncertainty Estimation for Object Detection in Open-Set Conditions"},{"key":"e_1_3_3_131_2","doi-asserted-by":"publisher","DOI":"10.1162\/089120100561737"},{"key":"e_1_3_3_132_2","article-title":"The hasyv2 dataset","author":"Thoma Martin","year":"2017","unstructured":"Martin Thoma. 2017. The hasyv2 dataset. arXiv:1701.08380. Retrieved from https:\/\/arxiv.org\/abs\/1701.08380.","journal-title":"arXiv:1701.08380"},{"key":"e_1_3_3_133_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W15-4007"},{"key":"e_1_3_3_134_2","doi-asserted-by":"publisher","DOI":"10.1109\/SLT.2010.5700816"},{"key":"e_1_3_3_135_2","article-title":"Automatic discovery of novel intents & domains from text utterances","author":"Vedula Nikhita","year":"2020","unstructured":"Nikhita Vedula, Rahul Gupta, Aman Alok, and Mukund Sridhar. 2020. 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Cybernet. 12, 6 (2021), 1627\u20131637.","journal-title":"Int. J. Mach. Learn. Cybernet."},{"key":"e_1_3_3_140_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3155451"},{"key":"e_1_3_3_141_2","article-title":"Bayesian embeddings for few-shot open world recognition","author":"Willes John","year":"2021","unstructured":"John Willes, James Harrison, Ali Harakeh, Chelsea Finn, Marco Pavone, and Steven Waslander. 2021. Bayesian embeddings for few-shot open world recognition. arXiv:2107.13682. Retrieved from https:\/\/arxiv.org\/abs\/2107.13682.","journal-title":"arXiv:2107.13682"},{"key":"e_1_3_3_142_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.79"},{"key":"e_1_3_3_143_2","first-page":"11329","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Wu Qitian","year":"2021","unstructured":"Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Junchi Yan, and Hongyuan Zha. 2021. Towards open-world recommendation: An inductive model-based collaborative filtering approach. In Proceedings of the International Conference on Machine Learning. 11329\u201311339."},{"key":"e_1_3_3_144_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00046"},{"key":"e_1_3_3_145_2","unstructured":"Zhi-Fan Wu Tong Wei Jianwen Jiang Chaojie Mao Mingqian Tang and Yu-Feng Li. 2021. NGC: A unified framework for learning with open-world noisy data. In Proceedings of the IEEE\/CVF International Conference on Computer Vision . 62\u201371."},{"key":"e_1_3_3_146_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.2307\/25148784","article-title":"E-commerce product recommendation agents: Use, characteristics, and impact","author":"Xiao Bo","year":"2007","unstructured":"Bo Xiao and Izak Benbasat. 2007. E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quart. (2007), 137\u2013209.","journal-title":"MIS Quart."},{"key":"e_1_3_3_147_2","article-title":"Fashion-mnist: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao Han","year":"2017","unstructured":"Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017. Fashion-mnist: A novel image dataset for benchmarking machine learning algorithms. arXiv:1708.07747. Retrieved from https:\/\/arxiv.org\/abs\/1708.07747.","journal-title":"arXiv:1708.07747"},{"key":"e_1_3_3_148_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10329"},{"key":"e_1_3_3_149_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313644"},{"key":"e_1_3_3_150_2","doi-asserted-by":"publisher","DOI":"10.5555\/2464909"},{"issue":"2","key":"e_1_3_3_151_2","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s12239-014-0034-6","article-title":"In-vehicle technology for self-driving cars: Advantages and challenges for aging drivers","volume":"15","author":"Yang J.","year":"2014","unstructured":"J. Yang and Joseph. Coughlin. 2014. In-vehicle technology for self-driving cars: Advantages and challenges for aging drivers. Int. J. Automot. Technol. 15, 2 (2014), 333\u2013340.","journal-title":"Int. J. Automot. 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