Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

17 papers of 8,653Sort Recent · Most cited
  1. 2025
    Efficient Self-Supervised Continual Learning with Progressive Task-Correlated Layer FreezingLi Yang, Sen Lin, Fan Zhang … Deliang FanInternational Symposium on Quality Electronic Design (ISQED) · University of North Carolina at Charlotte · University of Houston · +3
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  2. 2021
    Continual Learning of Generative Models With Limited Data: From Wasserstein-1 Barycenter to Adaptive CoalescenceMehmet Dedeoğlu, Sen Lin, Zhaofeng Zhang, Junshan ZhangTNNLS · Arizona State University · The Ohio State University · +1
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  3. 2022
    Efficient Multi-task Adaption for Crossbar-based In-Memory ComputingFan Zhang, Li Yang, Deliang FanAsilomar Conference on Signals, Systems, and Computers · Arizona State University
  4. 2022
    Efficient continual learning at the edge with progressive segmented trainingXiaocong Du, Shreyas Kolala Venkataramanaiah, Zheng Li … Yu CaoNeuromorphic Computing and Engineering · Arizona State University · Oak Ridge National Laboratory
  5. 2022
    Continual Learning for Activity RecognitionRamesh Kumar Sah, Seyed Iman Mirzadeh, Hassan GhasemzadehAnnual International Conference of the IEEE Engineering i… · Washington State University · Arizona State University
  6. 2022
    XST: A Crossbar Column-wise Sparse Training for Efficient Continual LearningFan Zhang, Yang Li, Jian Meng … Deliang FanDesign, Automation & Test in Europe Conference &… · Arizona State University
  7. 2022
    Graph Few-shot Class-incremental LearningZhen Tan, Kaize Ding, Ruocheng Guo, Huan LiuWSDM · Arizona State University · City University of Hong Kong
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  8. 2022
    XBM: A Crossbar Column-wise Binary Mask Learning Method for Efficient Multiple Task AdaptionFan Zhang, Yang Li, Jian Meng … Deliang FanAsia and South Pacific Design Automation Conference (ASP-… · Arizona State University
  9. 2022
    Self-supervised Novelty Detection for Continual Learning: A Gradient-Based Approach Boosted by Binary ClassificationJingbo Sun, Li Yang, Jiaxin Zhang … Yu CaoSpringer LNCS · Arizona State University · Oak Ridge National Laboratory · +1
  10. 2021
    Evolutionary NAS in Light of Model Stability for Accurate Continual LearningXiaocong Du, Zheng Li, Jingbo Sun … Yu CaoIJCNN · Arizona State University · Oak Ridge National Laboratory
  11. 2021
    KSM: Fast Multiple Task Adaption via Kernel-wise Soft Mask LearningLi Yang, Zhezhi He, Junshan Zhang, Deliang FanCVPR · Arizona State University
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  12. 2020
    Online Knowledge Acquisition with the Selective Inherited ModelXiaocong Du, Shreyas Kolala Venkataramanaiah, Zheng Li … Yu CaoIJCNN · Arizona State University · Oak Ridge National Laboratory
  13. 2020
    Noise-based Selection of Robust Inherited Model for Accurate Continual LearningXiaocong Du, Zheng Li, Jae-sun Seo … Yu CaoCVPR · Arizona State University · Oak Ridge National Laboratory
  14. 2020
    Class-incremental Learning via Deep Model ConsolidationJunting Zhang, Jie Zhang, Shalini Ghosh … C.‐C. Jay KuoWACV · University of Southern California · California Southern University · +3
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  15. 2020
    Regularize, Expand and Compress: NonExpansive Continual LearningJie Zhang, Junting Zhang, Shalini Ghosh … Yalin WangWACV · University of Southern California · Arizona State University · +1
  16. 2019
    Single-Net Continual Learning with Progressive Segmented TrainingXiaocong Du, Gouranga Charan, Frank Liu, Yu CaoICML · Arizona State University · Oak Ridge National Laboratory
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  17. 2010
    An Autonomous Incremental Learning Algorithm for Radial Basis Function NetworksSeiichi Ozawa, Toshihisa Tabuchi, Sho Nakasaka, Asim RoyJournal of Intelligent Learning Systems and Applications · Kobe University · Arizona State University
About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.