{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:06:41Z","timestamp":1784736401400,"version":"3.55.0"},"reference-count":92,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12125104"],"award-info":[{"award-number":["12125104"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12326615"],"award-info":[{"award-number":["12326615"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2022B0303020003"],"award-info":[{"award-number":["2022B0303020003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1109\/tpami.2024.3522258","type":"journal-article","created":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T19:11:10Z","timestamp":1735240270000},"page":"2563-2580","source":"Crossref","is-referenced-by-count":5,"title":["Training Networks in Null Space of Feature Covariance With Self-Supervision for Incremental Learning"],"prefix":"10.1109","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4586-8287","authenticated-orcid":false,"given":"Shipeng","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2090-2592","authenticated-orcid":false,"given":"Xiaorong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7206-0641","authenticated-orcid":false,"given":"Jian","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4066-2338","authenticated-orcid":false,"given":"Zongben","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.479"},{"key":"ref3","first-page":"10","article-title":"Deep ADMM-Net for compressive sensing MRI","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Yan"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00581"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref9","article-title":"Three scenarios for continual learning","author":"Van de Ven","year":"2019"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3057446"},{"key":"ref11","article-title":"Recent advances of continual learning in computer vision: An overview","author":"Qu","year":"2021"},{"key":"ref12","article-title":"Distilling the knowledge in a neural network","volume-title":"Proc. Adv. Neural Inform. Process. Syst. Workshop","author":"Hinton"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00399"},{"key":"ref14","first-page":"3925","article-title":"Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00092"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_15"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00046"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref19","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref20","article-title":"FearNet: Brain-inspired model for incremental learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kemker"},{"key":"ref21","first-page":"2990","article-title":"Continual learning with deep generative replay","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Shin"},{"key":"ref22","first-page":"5962","article-title":"Memory replay GANs: Learning to generate new categories without forgetting","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Wu"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00672"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0080-x"},{"key":"ref25","article-title":"Gradient projection memory for continual learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Saha"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00025"},{"key":"ref27","first-page":"5714","article-title":"Self-supervised label augmentation via input transformations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lee"},{"key":"ref28","first-page":"3987","article-title":"Continual learning through synaptic intelligence","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zenke"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01252-6_33"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5981"},{"key":"ref31","article-title":"Continual learning with adaptive weights (claw)","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Adel"},{"key":"ref32","article-title":"Variational continual learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Nguyen"},{"key":"ref33","first-page":"4652","article-title":"Overcoming catastrophic forgetting by incremental moment matching","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Lee"},{"key":"ref34","first-page":"75","article-title":"Uncertainty-guided continual learning in Bayesian neural networks","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshop","author":"Ebrahimi"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00528"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00040"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00285"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01226"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00121"},{"key":"ref40","article-title":"Learning to learn without forgetting by maximizing transfer and minimizing interference","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Riemer"},{"key":"ref41","article-title":"Continual learning with tiny episodic memories","volume-title":"Proc. Int. Conf. Mach. Learn. Workshops","author":"Chaudhry"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01322"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00067"},{"key":"ref44","article-title":"Overcoming catastrophic forgetting for continual learning via model adaptation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hu"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01158"},{"key":"ref46","first-page":"13 669","article-title":"Compacting, picking and growing for unforgetting continual learning","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Hung"},{"key":"ref47","article-title":"Lifelong learning with dynamically expandable networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yoon"},{"key":"ref48","first-page":"15579","article-title":"Calibrating CNNs for lifelong learning","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Singh"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_5"},{"key":"ref50","first-page":"12 669","article-title":"Random path selection for continual learning","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Rajasegaran"},{"key":"ref51","first-page":"4548","article-title":"Overcoming catastrophic forgetting with hard attention to the task","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Serra"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00257"},{"key":"ref53","first-page":"6467","article-title":"Gradient episodic memory for continual learning","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Lopez-Paz"},{"key":"ref54","article-title":"Efficient lifelong learning with a-gem","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chaudhry"},{"key":"ref55","article-title":"Overcoming catastrophic interference using conceptor-aided backpropagation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"He"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"ref60","article-title":"Unsupervised representation learning by predicting image rotations","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gidaris"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00156"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00815"},{"key":"ref65","first-page":"15 663","article-title":"Using self-supervised learning can improve model robustness and uncertainty","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Hendrycks"},{"key":"ref66","first-page":"19290","article-title":"Rethinking the value of labels for improving class-imbalanced learning","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Yang"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00940"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898719512"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00701"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i3.16334"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00810"},{"key":"ref76","article-title":"TRGP: Trust region gradient projection for continual learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lin"},{"key":"ref77","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref78","article-title":"Tiny ImageNet challenge","author":"Wu","year":"2017"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01640"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref81","article-title":"Reading digits in natural images with unsupervised feature learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. Workshops","author":"Netzer","year":"2011"},{"key":"ref82","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"ref83","first-page":"3637","article-title":"Matching networks for one shot learning","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Vinyals"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00398"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_27"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295163"},{"key":"ref87","article-title":"Prototypical contrastive learning of unsupervised representations","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00610"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00459"},{"key":"ref90","first-page":"901","article-title":"Weight normalization: A simple reparameterization to accelerate training of deep neural networks","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Salimans"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01418-6_38"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00908"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/10916529\/10816176.pdf?arnumber=10816176","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T18:42:52Z","timestamp":1741372972000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10816176\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":92,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2024.3522258","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}