{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T19:02:57Z","timestamp":1773342177020,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72071145"],"award-info":[{"award-number":["72071145"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2019YFB1704402"],"award-info":[{"award-number":["2019YFB1704402"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10489-022-04239-z","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T11:28:31Z","timestamp":1666351711000},"page":"14128-14145","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A robust and anti-forgettiable model for class-incremental learning"],"prefix":"10.1007","volume":"53","author":[{"given":"Jianting","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9714-1210","authenticated-orcid":false,"given":"Yang","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"4239_CR1","unstructured":"Aljundi R, Lin M, Goujaud B et al (2019) Gradient based sample selection for online continual learning. In: NeurIPS, pp 11,816\u201311,825"},{"key":"4239_CR2","unstructured":"Buzzega P, Boschini M, Porrello A et al (2020) Dark experience for general continual learning: a strong, simple baseline. In: NeurIPS, pp 15,920\u201315,930"},{"key":"4239_CR3","doi-asserted-by":"publisher","unstructured":"Buzzega P, Boschini M, Porrello A et al (2021) Rethinking experience replay: a bag of tricks for continual learning. In: ICPR, pp 2180\u20132187. https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412614","DOI":"10.1109\/ICPR48806.2021.9412614"},{"key":"4239_CR4","doi-asserted-by":"publisher","unstructured":"Castro FM, Mar\u00edn-Jim\u00e9nez MJ, Guil N et al (2018) End-to-end incremental learning. In: ECCV, pp 241\u2013257. https:\/\/doi.org\/10.1007\/978-3-030-01258-8_15","DOI":"10.1007\/978-3-030-01258-8_15"},{"key":"4239_CR5","unstructured":"Chaudhry A, Ranzato M, Rohrbach M et al (2019) Efficient lifelong learning with A-GEM. In: ICLR"},{"key":"4239_CR6","unstructured":"Deecke L, Murray I, Bilen H (2019) Mode normalization. In: ICLR"},{"issue":"5","key":"4239_CR7","doi-asserted-by":"publisher","first-page":"5159","DOI":"10.1007\/s10489-021-02643-5","volume":"52","author":"J Ding","year":"2022","unstructured":"Ding J (2022) Incremental learning with open set based discrimination enhancement. Appl Intell 52(5):5159\u20135172. https:\/\/doi.org\/10.1007\/s10489-021-02643-5","journal-title":"Appl Intell"},{"key":"4239_CR8","doi-asserted-by":"publisher","unstructured":"Douillard A, Cord M, Ollion C et al (2020) Podnet: pooled outputs distillation for small-tasks incremental learning. In: ECCV, pp 86\u2013102. https:\/\/doi.org\/10.1007\/978-3-030-58565-5_6","DOI":"10.1007\/978-3-030-58565-5_6"},{"key":"4239_CR9","unstructured":"Farajtabar M, Azizan N, Mott A et al (2020) Orthogonal gradient descent for continual learning. In: AISTATS, pp 3762\u20133773"},{"key":"4239_CR10","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.neunet.2020.05.011","volume":"128","author":"HM Fayek","year":"2020","unstructured":"Fayek HM, Cavedon L, Wu HR (2020) Progressive learning: a deep learning framework for continual learning. Neural Netw 128:345\u2013357. https:\/\/doi.org\/10.1016\/j.neunet.2020.05.011","journal-title":"Neural Netw"},{"key":"4239_CR11","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.neunet.2021.08.011","volume":"144","author":"Y Gao","year":"2021","unstructured":"Gao Y, Ascoli GA, Zhao L (2021) Schematic memory persistence and transience for efficient and robust continual learning. Neural Netw 144:49\u201360. https:\/\/doi.org\/10.1016\/j.neunet.2021.08.011","journal-title":"Neural Netw"},{"issue":"7","key":"4239_CR12","first-page":"38","volume":"14","author":"G Hinton","year":"2015","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. Comput Sci 14(7):38\u201339","journal-title":"Comput Sci"},{"key":"4239_CR13","doi-asserted-by":"publisher","unstructured":"Hou S, Pan X, Loy CC et al (2019) Learning a unified classifier incrementally via rebalancing. In: CVPR, pp 831\u2013839. https:\/\/doi.org\/10.1109\/CVPR.2019.00092","DOI":"10.1109\/CVPR.2019.00092"},{"key":"4239_CR14","unstructured":"Ioffe S (2017) Batch renormalization: towards reducing minibatch dependence in batch-normalized models. In: NIPS, pp 1945\u20131953"},{"key":"4239_CR15","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML, pp 448\u2013456"},{"key":"4239_CR16","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.knosys.2021.107589","volume":"234","author":"Z Ji","year":"2021","unstructured":"Ji Z, Liu J, Wang Q et al (2021) Coordinating experience replay: a harmonious experience retention approach for continual learning. Knowl-Based Syst 234:107\u2013589. https:\/\/doi.org\/10.1016\/j.knosys.2021.107589","journal-title":"Knowl-Based Syst"},{"issue":"4","key":"4239_CR17","doi-asserted-by":"publisher","first-page":"4527","DOI":"10.1007\/s10489-021-02543-8","volume":"52","author":"M Jiang","year":"2022","unstructured":"Jiang M, Li F, Liu L (2022) Continual meta-learning algorithm. Appl Intell 52(4):4527\u20134542. https:\/\/doi.org\/10.1007\/s10489-021-02543-8","journal-title":"Appl Intell"},{"key":"4239_CR18","unstructured":"Kemker R, Kanan C (2018) Fearnet: Brain-inspired model for incremental learning. In: ICLR"},{"issue":"13","key":"4239_CR19","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick J, Pascanu R, Rabinowitz N et al (2017) Overcoming catastrophic forgetting in neural networks. Proc National Acad Sci 114(13):3521\u20133526. https:\/\/doi.org\/10.1073\/pnas.1611835114","journal-title":"Proc National Acad Sci"},{"issue":"6266","key":"4239_CR20","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1126\/science.aab3050","volume":"350","author":"BM Lake","year":"2015","unstructured":"Lake BM, Salakhutdinov R, Tenenbaum JB (2015) Human-level concept learning through probabilistic program induction. Science 350(6266):1332\u20131338. https:\/\/doi.org\/10.1126\/science.aab3050","journal-title":"Science"},{"issue":"12","key":"4239_CR21","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2018","unstructured":"Li Z, Hoiem D (2018) Learning without forgetting. IEEE Trans Pattern Anal Mach Intell 40 (12):2935\u20132947. https:\/\/doi.org\/10.1109\/TPAMI.2017.2773081","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4239_CR22","doi-asserted-by":"publisher","unstructured":"Lomonaco V, Maltoni D, Pellegrini L (2020) Rehearsal-free continual learning over small non-i.i.d. batches. In: CVPR workshop, pp 989\u2013998. https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00131","DOI":"10.1109\/CVPRW50498.2020.00131"},{"key":"4239_CR23","unstructured":"Lopez-Paz D, Ranzato M (2017) Gradient episodic memory for continual learning. In: NeurIPS, pp 6467\u20136476"},{"key":"4239_CR24","doi-asserted-by":"publisher","unstructured":"Mai Z, Li R, Kim H et al (2021) Supervised contrastive replay: revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR workshop, pp 3589\u20133599. https:\/\/doi.org\/10.1109\/CVPRW53098.2021.00398","DOI":"10.1109\/CVPRW53098.2021.00398"},{"key":"4239_CR25","doi-asserted-by":"publisher","unstructured":"McCloskey M, Cohen NJ, Bower GH (1989) Catastrophic interference in connectionist networks: the sequential learning problem, vol 24, Academic Press, pp 109\u2013165.. https:\/\/doi.org\/10.1016\/S0079-7421(08)60536-8","DOI":"10.1016\/S0079-7421(08)60536-8"},{"key":"4239_CR26","unstructured":"Pham Q, Liu C, HOI S (2022) Continual normalization: rethinking batch normalization for online continual learning. In: ICLR"},{"key":"4239_CR27","doi-asserted-by":"publisher","unstructured":"Rebuffi S, Kolesnikov A, Sperl G et al (2017) icarl: incremental classifier and representation learning. In: CVPR, pp 5533\u20135542. https:\/\/doi.org\/10.1109\/CVPR.2017.587","DOI":"10.1109\/CVPR.2017.587"},{"key":"4239_CR28","unstructured":"Riemer M, Cases I, Ajemian R et al (2019) Learning to learn without forgetting by maximizing transfer and minimizing interference. In: ICLR"},{"issue":"3","key":"4239_CR29","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1109\/TPAMI.2018.2884462","volume":"42","author":"A Rosenfeld","year":"2020","unstructured":"Rosenfeld A, Tsotsos JK (2020) Incremental learning through deep adaptation. IEEE Trans Pattern Anal Mach Intell 42(3):651\u2013663. https:\/\/doi.org\/10.1109\/TPAMI.2018.2884462","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4239_CR30","unstructured":"Saha G, Garg I, Roy K (2021) Gradient projection memory for continual learning. In: ICLR"},{"key":"4239_CR31","unstructured":"Serr\u00e0 J, Suris D, Miron M et al (2018) Overcoming catastrophic forgetting with hard attention to the task. In: ICML, pp 4555\u20134564"},{"key":"4239_CR32","doi-asserted-by":"crossref","unstructured":"Shim D, Mai Z, Jeong J et al (2021) Online class-incremental continual learning with adversarial shapley value. In: AAAI, pp 9630\u20139638","DOI":"10.1609\/aaai.v35i11.17159"},{"key":"4239_CR33","unstructured":"Shin H, Lee JK, Kim J et al (2017) Continual learning with deep generative replay. In: NeurIPS, pp 2990\u20132999"},{"key":"4239_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2021.01.078","volume":"439","author":"G Sokar","year":"2021","unstructured":"Sokar G, Mocanu DC, Pechenizkiy M (2021) Spacenet: make free space for continual learning. Neurocomputing 439:1\u201311. https:\/\/doi.org\/10.1016\/j.neucom.2021.01.078","journal-title":"Neurocomputing"},{"key":"4239_CR35","unstructured":"Vinyals O, Blundell C, Lillicrap T et al (2016) Matching networks for one shot learning. In: NeurIPS, pp 3630\u20133638"},{"key":"4239_CR36","unstructured":"Wu C, Herranz L, Liu X et al (2018) Memory replay gans: learning to generate new categories without forgetting. In: NeurIPS, pp 5966\u20135976"},{"key":"4239_CR37","doi-asserted-by":"publisher","unstructured":"Wu Y, Chen Y, Wang L et al (2019) Large scale incremental learning. In: CVPR, pp 374\u2013382. https:\/\/doi.org\/10.1109\/CVPR.2019.00046","DOI":"10.1109\/CVPR.2019.00046"},{"key":"4239_CR38","unstructured":"Xiao H, Rasul K, Vollgraf R (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. CoRR arXiv:1708.07747"},{"key":"4239_CR39","doi-asserted-by":"crossref","unstructured":"Yan S, Xie J, He X (2021) DER: dynamically expandable representation for class incremental learning. In: CVPR, pp 3014\u20133023","DOI":"10.1109\/CVPR46437.2021.00303"},{"issue":"1","key":"4239_CR40","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/s10489-021-02385-4","volume":"52","author":"H Yu","year":"2022","unstructured":"Yu H, Dai Q (2022) Dwe-il: a new incremental learning algorithm for non-stationary time series prediction via dynamically weighting ensemble learning. Appl Intell 52(1):174\u2013194. https:\/\/doi.org\/10.1007\/s10489-021-02385-4","journal-title":"Appl Intell"},{"key":"4239_CR41","unstructured":"Zenke F, Poole B, Ganguli S (2017) Continual learning through synaptic intelligence. In: ICML, pp 3987\u20133995"},{"key":"4239_CR42","doi-asserted-by":"publisher","unstructured":"Zhao B, Xiao X, Gan G et al (2020) Maintaining discrimination and fairness in class incremental learning. In: CVPR, pp 13,205\u201313,214. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01322","DOI":"10.1109\/CVPR42600.2020.01322"},{"key":"4239_CR43","unstructured":"Zhou D, Wang F, Ye H et al (2021) Pycil: a python toolbox for class-incremental learning. CoRR arXiv:2112.12533"},{"key":"4239_CR44","doi-asserted-by":"crossref","unstructured":"Zhu F, Zhang XY, Wang C et al (2021) Prototype augmentation and self-supervision for incremental learning. In: CVPR, pp 5871\u20135880","DOI":"10.1109\/CVPR46437.2021.00581"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04239-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04239-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04239-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T06:34:24Z","timestamp":1685514864000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04239-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,21]]},"references-count":44,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["4239"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04239-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,21]]},"assertion":[{"value":"4 October 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}