{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:39:14Z","timestamp":1770748754292,"version":"3.50.0"},"publisher-location":"Cham","reference-count":65,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031463075","type":"print"},{"value":"9783031463082","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46308-2_18","type":"book-chapter","created":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T18:01:24Z","timestamp":1698602484000},"page":"212-224","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Recent Advances in\u00a0Class-Incremental Learning"],"prefix":"10.1007","author":[{"given":"Dejie","family":"Yang","sequence":"first","affiliation":[]},{"given":"Minghang","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Weishuai","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Sizhe","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Aljundi, R., Chakravarty, P., Tuytelaars, T.: Expert gate: lifelong learning with a network of experts. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.753"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Bertugli, A., Vincenzi, S., Calderara, S., Passerini, A.: Generalising via meta-examples for continual learning in the wild. In: Machine Learning, Optimization, and Data Science: 8th International Workshop, LOD (2022)","DOI":"10.1007\/978-3-031-25599-1_31"},{"key":"18_CR3","unstructured":"Bhat, P., Zonooz, B., Arani, E.: Task-aware information routing from common representation space in lifelong learning. arXiv (2023)"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Cermelli, F., Mancini, M., Bul\u00f2, S.R., Ricci, E., Caputo, B.: Modeling the background for incremental learning in semantic segmentation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00925"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Chamikara, M.A.P., Bert\u00f3k, P., Liu, D., Camtepe, S., Khalil, I.: Efficient data perturbation for privacy preserving and accurate data stream mining. Pervasive Mob. Comput. (2018)","DOI":"10.1016\/j.pmcj.2018.05.003"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Chen, K., Liu, S., Wang, R., Zheng, W.S.: Adaptively integrated knowledge distillation and prediction uncertainty for continual learning. arXiv (2023)","DOI":"10.1109\/CAC59555.2023.10450726"},{"key":"18_CR7","unstructured":"Chrysakis, A., Moens, M.F.: Online bias correction for task-free continual learning. In: ICLR (2022)"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Cui, Y., Deng, W., Chen, H., Liu, L.: Uncertainty-aware distillation for semi-supervised few-shot class-incremental learning. arXiv (2023)","DOI":"10.1109\/TNNLS.2023.3277018"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Dai, X., et al.: Closed-loop data transcription to an LDR via minimaxing rate reduction. arXiv (2021)","DOI":"10.3390\/e24040456"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"18_CR11","unstructured":"Deng, L.: The mNIST database of handwritten digit images for machine learning research. IEEE Sig. Process. Mag. (2012)"},{"key":"18_CR12","unstructured":"Ding, Y., Liu, L., Tian, C., Yang, J., Ding, H.: Don\u2019t stop learning: towards continual learning for the clip model. arXiv (2022)"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Dong, J., et al.: Federated class-incremental learning. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00992"},{"key":"18_CR14","unstructured":"Gaya, J.B., Doan, T.V., Caccia, L., Soulier, L., Denoyer, L., Raileanu, R.: Building a subspace of policies for scalable continual learning. arXiv (2022)"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Golab, L., \u00d6zsu, M.T.: Issues in data stream management. ACM SIGMOD Rec. (2003)","DOI":"10.1145\/776985.776986"},{"key":"18_CR16","unstructured":"Han, K., et al.: A survey on vision transformer. IEEE TPAMI (2022)"},{"key":"18_CR17","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv (2015)"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Hu, Z., Li, Y., Lyu, J., Gao, D., Vasconcelos, N.: Dense network expansion for class incremental learning. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01141"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Hurtado, J., Salvati, D., Semola, R., Bosio, M., Lomonaco, V.: Continual learning for predictive maintenance: overview and challenges. arXiv (2023)","DOI":"10.1016\/j.iswa.2023.200251"},{"key":"18_CR20","unstructured":"Jeon, M., Lee, H., Seong, Y., Kang, M.: Learning without prejudices: continual unbiased learning via benign and malignant forgetting. In: ICLR (2022)"},{"key":"18_CR21","unstructured":"Julian, R.C., Swanson, B., Sukhatme, G.S., Levine, S., Finn, C., Hausman, K.: Efficient adaptation for end-to-end vision-based robotic manipulation. arXiv (2020)"},{"key":"18_CR22","unstructured":"Kilickaya, M., van de Weijer, J., Asano, Y.M.: Towards label-efficient incremental learning: a survey. arXiv (2023)"},{"key":"18_CR23","unstructured":"Kim, D.Y., Han, D.J., Seo, J., Moon, J.: Warping the space: weight space rotation for class-incremental few-shot learning. In: ICLR (2022)"},{"key":"18_CR24","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. (2016)"},{"key":"18_CR25","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"18_CR26","unstructured":"Le, Y., Yang, X.: Tiny imagenet visual recognition challenge. CS 231N (2015)"},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Lee, J., Hong, H.G., Joo, D., Kim, J.: Continual learning with extended Kronecker-factored approximate curvature. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00902"},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"Lee, K.Y., Zhong, Y., Wang, Y.X.: Do pre-trained models benefit equally in continual learning? In: WACV (2023)","DOI":"10.1109\/WACV56688.2023.00642"},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"Lei, S.W., et al.: Symbolic replay: scene graph as prompt for continual learning on VQA task. arXiv (2022)","DOI":"10.1609\/aaai.v37i1.25208"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., Rodr\u00edguez, N.D.: Continual learning for robotics: definition, framework, learning strategies, opportunities and challenges. Inf. Fusion (2020)","DOI":"10.1016\/j.inffus.2019.12.004"},{"key":"18_CR31","doi-asserted-by":"crossref","unstructured":"Li, Y., Bai, L., Liang, Z., Du, H.: Incremental label propagation for data sets with imbalanced labels. Neurocomputing (2023)","DOI":"10.1016\/j.neucom.2023.03.016"},{"key":"18_CR32","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Steering prototype with prompt-tuning for rehearsal-free continual learning. arXiv (2023)","DOI":"10.1109\/WACV57701.2024.00251"},{"key":"18_CR33","doi-asserted-by":"crossref","unstructured":"Liu, D., Lyu, F., Li, L., Xia, Z., Hu, F.: Centroid distance distillation for effective rehearsal in continual learning. arXiv (2023)","DOI":"10.1109\/ICASSP49357.2023.10094837"},{"key":"18_CR34","doi-asserted-by":"crossref","unstructured":"Liu, T., Ungar, L., Sedoc, J.: Continual learning for sentence representations using conceptors. arXiv (2019)","DOI":"10.18653\/v1\/N19-1331"},{"key":"18_CR35","doi-asserted-by":"crossref","unstructured":"Luo, Z., Liu, Y., Schiele, B., Sun, Q.: Class-incremental exemplar compression for class-incremental learning. arXiv (2023)","DOI":"10.1109\/CVPR52729.2023.01094"},{"key":"18_CR36","unstructured":"Ma, C., Ji, Z., Huang, Z., Shen, Y., Gao, M., Xu, J.: Progressive Voronoi diagram subdivision enables accurate data-free class-incremental learning. In: ICLR (2022)"},{"key":"18_CR37","unstructured":"Ma, Z., Hong, X., Liu, B., Wang, Y., Guo, P., Li, H.: Remind of the past: incremental learning with analogical prompts. arXiv (2023)"},{"key":"18_CR38","doi-asserted-by":"crossref","unstructured":"Masana, M., Liu, X., Twardowski, B., Menta, M., Bagdanov, A.D., van de Weijer, J.: Class-incremental learning: survey and performance evaluation on image classification. IEEE TPAMI (2022)","DOI":"10.1109\/TPAMI.2022.3213473"},{"key":"18_CR39","unstructured":"de Masson d\u2019Autume, C., Ruder, S., Kong, L., Yogatama, D.: Episodic memory in lifelong language learning. arXiv (2019)"},{"key":"18_CR40","doi-asserted-by":"crossref","unstructured":"McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: the sequential learning problem. In: Psychology of Learning and Motivation (1989)","DOI":"10.1016\/S0079-7421(08)60536-8"},{"key":"18_CR41","doi-asserted-by":"crossref","unstructured":"Michieli, U., Zanuttigh, P.: Incremental learning techniques for semantic segmentation. In: ICCV Workshop (2019)","DOI":"10.1109\/ICCVW.2019.00400"},{"key":"18_CR42","unstructured":"OpenAI: Gpt-4 technical report (2023)"},{"key":"18_CR43","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-R\u00faa, J.M., Zhu, X., Hospedales, T.M., Xiang, T.: Incremental few-shot object detection. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01386"},{"key":"18_CR44","doi-asserted-by":"crossref","unstructured":"Petit, G., Popescu, A., Schindler, H., Picard, D., Delezoide, B.: Fetril: feature translation for exemplar-free class-incremental learning. In: WACV (2023)","DOI":"10.1109\/WACV56688.2023.00390"},{"key":"18_CR45","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Xu, L., Wang, Z., Wu, Q., Meng, F., Li, H.: ISM-net: mining incremental semantics for class incremental learning. Neurocomputing (2023)","DOI":"10.2139\/ssrn.4179872"},{"key":"18_CR46","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: ICML (2021)"},{"key":"18_CR47","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: ICARL: incremental classifier and representation learning. In: ICCV (2017)","DOI":"10.1109\/CVPR.2017.587"},{"key":"18_CR48","doi-asserted-by":"crossref","unstructured":"Smith, J., et al.: Coda-prompt: continual decomposed attention-based prompting for rehearsal-free continual learning. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01146"},{"key":"18_CR49","doi-asserted-by":"crossref","unstructured":"Song, Z., Zhao, Y., Shi, Y., Peng, P., Yuan, L., Tian, Y.: Learning with fantasy: semantic-aware virtual contrastive constraint for few-shot class-incremental learning. arXiv (2023)","DOI":"10.1109\/CVPR52729.2023.02316"},{"key":"18_CR50","unstructured":"Sun, F.K., Ho, C.H., Yi Lee, H.: Lamol: language modeling for lifelong language learning. In: ICLR (2019)"},{"key":"18_CR51","unstructured":"Tong, S., Dai, X., Wu, Z., Li, M., Yi, B., Ma, Y.: Incremental learning of structured memory via closed-loop transcription. arXiv (2022)"},{"key":"18_CR52","doi-asserted-by":"crossref","unstructured":"van de Ven, G.M., Tuytelaars, T., Tolias, A.S.: Three types of incremental learning. Nat. Mach. Intell. (2022)","DOI":"10.1038\/s42256-022-00568-3"},{"key":"18_CR53","doi-asserted-by":"crossref","unstructured":"Verwimp, E., et al.: Clad: a realistic continual learning benchmark for autonomous driving. Neural Netw. (2023)","DOI":"10.1016\/j.neunet.2023.02.001"},{"key":"18_CR54","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)"},{"key":"18_CR55","unstructured":"Wang, F.Y., Zhou, D.W., Liu, L., Ye, H.J., Bian, Y., Zhan, D.C., Zhao, P.: Beef: bi-compatible classincremental learning via energy-based expansion and fusion (2023)"},{"key":"18_CR56","unstructured":"Wang, L., Zhang, X., Su, H., Zhu, J.: A comprehensive survey of continual learning: theory, method and application. arXiv (2023)"},{"key":"18_CR57","doi-asserted-by":"crossref","unstructured":"Xu, X., Wang, Z., Fu, Z., Guo, W., Chi, Z., Li, D.: Flexible few-shot class-incremental learning with prototype container. Neural Comput. Appl. (2023)","DOI":"10.1007\/s00521-023-08272-y"},{"key":"18_CR58","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhou, D., Zhan, D., Xiong, H., Jiang, Y., Yang, J.: Cost-effective incremental deep model: matching model capacity with the least sampling. IEEE TKDE (2023)","DOI":"10.1109\/TKDE.2021.3132622"},{"key":"18_CR59","unstructured":"Yang, Y., Yuan, H., Li, X., Lin, Z., Torr, P., Tao, D.: Neural collapse inspired feature-classifier alignment for few-shot class incremental learning. arXiv (2023)"},{"key":"18_CR60","unstructured":"Yoon, J., Yang, E., Lee, J., Hwang, S.J.: Lifelong learning with dynamically expandable networks. arXiv (2017)"},{"key":"18_CR61","unstructured":"Zenke, F., Poole, B., Ganguli, S.: Continual learning through synaptic intelligence. In: ICML (2017)"},{"key":"18_CR62","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Ma, M., Wang, K., Qin, Z., Yue, X., You, Y.: Preventing zero-shot transfer degradation in continual learning of vision-language models. arXiv (2023)","DOI":"10.1109\/ICCV51070.2023.01752"},{"key":"18_CR63","doi-asserted-by":"crossref","unstructured":"Zhou, D.W., Wang, Q.W., Qi, Z.H., Ye, H.J., Zhan, D.C., Liu, Z.: Deep class-incremental learning: a survey. arXiv (2023)","DOI":"10.1109\/TPAMI.2024.3429383"},{"key":"18_CR64","unstructured":"Zhou, D.W., Wang, Q., Ye, H.J., Chuan Zhan, D.: A model or 603 exemplars: towards memory-efficient class-incremental learning. arXiv (2022)"},{"key":"18_CR65","doi-asserted-by":"crossref","unstructured":"Zhou, D.W., Ye, H.J., Zhan, D.C., Liu, Z.: Revisiting class-incremental learning with pre-trained models: generalizability and adaptivity are all you need. arXiv (2023)","DOI":"10.1007\/s11263-024-02218-0"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46308-2_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:47:54Z","timestamp":1730422074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46308-2_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031463075","9783031463082"],"references-count":65,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46308-2_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icig2023.csig.org.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference Management Toolkit","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"409","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"166","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}