{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T18:08:40Z","timestamp":1783966120588,"version":"3.55.0"},"reference-count":73,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3264769","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T17:52:59Z","timestamp":1680717179000},"page":"36692-36701","source":"Crossref","is-referenced-by-count":4,"title":["Meta-Learning Based Tasks Similarity Representation for Cross Domain Lifelong Learning"],"prefix":"10.1109","volume":"11","author":[{"given":"Mingge","family":"Shen","sequence":"first","affiliation":[{"name":"College of Intelligent Equipment and the Zhejiang College of Security Technology, Wenzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dehu","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Architecture and Energy Engineering, Wenzhou University of Technology, Wenzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7090-414X","authenticated-orcid":false,"given":"Teng","family":"Ren","sequence":"additional","affiliation":[{"name":"Dominican University, River Forest, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2776930"},{"key":"ref2","article-title":"Language models are few-shot learners","author":"Brown","year":"2020","journal-title":"arXiv:2005.14165"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3027923"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2777827"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3037258"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3006097"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3197769"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref11","first-page":"350","article-title":"Experience replay for continual learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Rolnick"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00040"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00810"},{"key":"ref14","article-title":"PathNet: Evolution channels gradient descent in super neural networks","author":"Fernando","year":"2017","journal-title":"arXiv:1701.08734"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00905"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5529-2_1"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_12"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58583-9_8"},{"key":"ref19","article-title":"Cross-domain few-shot classification via learned feature-wise transformation","author":"Tseng","year":"2020","journal-title":"arXiv:2001.08735"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3057446"},{"key":"ref21","article-title":"Re-evaluating continual learning scenarios: A categorization and case for strong baselines","author":"Hsu","year":"2018","journal-title":"arXiv:1810.12488"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1080\/09540099208946624"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.4324\/9781315789354-58"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-47922-8_8"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1080\/09540099550039318"},{"key":"ref27","first-page":"489","article-title":"Discovering structure in multiple learning tasks: The TC algorithm","volume-title":"Proc. 13th Int. Conf. Mach. Learn.","volume":"96","author":"Thrun"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273606"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v24i1.7519"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/S0764-4469(97)82472-9"},{"key":"ref31","first-page":"866","article-title":"JumpNet: A multiple-memory connectionist architecture","volume-title":"Proc. 15 th Annu. Conf. Cognit. Sci. Soc.","volume":"24","author":"Rueckl"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1080\/095400997116595"},{"key":"ref33","first-page":"507","article-title":"ELLA: An efficient lifelong learning algorithm","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ruvolo"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-008-5050-1"},{"key":"ref35","first-page":"613","article-title":"Infinite predictor subspace models for multitask learning","volume-title":"Proc. 13th Int. Conf. Artif. Intell. Statist. Workshop Conf.","author":"Rai"},{"key":"ref36","article-title":"Learning task grouping and overlap in multi-task learning","author":"Kumar","year":"2012","journal-title":"arXiv:1206.6417"},{"key":"ref37","first-page":"991","article-title":"A PAC-Bayesian bound for lifelong learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pentina"},{"key":"ref38","first-page":"261","article-title":"Regret bounds for lifelong learning","volume-title":"Proc. Artif. Intell. Statist.","author":"Alquier"},{"key":"ref39","first-page":"1540","article-title":"Lifelong learning with non-i.i.d. tasks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Pentina"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00814"},{"key":"ref41","article-title":"Class-incremental learning: Survey and performance evaluation on image classification","author":"Masana","year":"2020","journal-title":"arXiv:2010.15277"},{"key":"ref42","first-page":"6467","article-title":"Gradient episodic memory for continual learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lopez-Paz"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref44","article-title":"Continual learning with deep generative replay","author":"Shin","year":"2017","journal-title":"arXiv:1705.08690"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01052"},{"key":"ref46","first-page":"3987","article-title":"Continual learning through synaptic intelligence","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zenke"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2017.07.024"},{"key":"ref49","article-title":"Uncertainty-based continual learning with adaptive regularization","author":"Ahn","year":"2019","journal-title":"arXiv:1905.11614"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01447"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01151"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.753"},{"key":"ref53","article-title":"Progressive neural networks","author":"Rusu","year":"2016","journal-title":"arXiv:1606.04671"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-25858-z"},{"key":"ref55","first-page":"1842","article-title":"Meta-learning with memory-augmented neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Santoro"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_28"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00567"},{"key":"ref58","article-title":"Prototypical networks for few-shot learning","author":"Snell","year":"2017","journal-title":"arXiv:1703.05175"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01360"},{"key":"ref60","first-page":"17571","article-title":"Continuous meta-learning without tasks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Harrison"},{"key":"ref61","volume-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2014.7025068"},{"key":"ref63","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref64","first-page":"108","article-title":"Deep learning architectures for hard character classification","volume-title":"Proc. Int. Conf. Artif. Intell. (ICAI). Steering Committee World Congr. Comput. Sci., Comput.","author":"Bui"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref66","volume-title":"Reading digits in natural images with unsupervised feature learning","author":"Netzer","year":"2011"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"ref68","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"25","author":"Krizhevsky"},{"key":"ref69","first-page":"1","article-title":"Continuous metalearning without tasks","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","author":"Harrison"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533880"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5884"},{"key":"ref73","article-title":"Pseudo-recursal: Solving the catastrophic forgetting problem in deep neural networks","volume":"2","author":"Atkinson","year":"2018","journal-title":"arXiv:1802.03875"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10092577.pdf?arnumber=10092577","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T21:29:13Z","timestamp":1709242153000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10092577\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":73,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3264769","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}