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.

7 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
    Beyond Not-Forgetting: Continual Learning with Backward Knowledge TransferSen Lin, Li Yang, Deliang Fan, Junshan ZhangNeurIPS
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  4. 2022
    TRGP: Trust Region Gradient Projection for Continual LearningSen Lin, Li Yang, Deliang Fan, Junshan ZhangICLR
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  5. 2022
    CL-LSG: Continual Learning via Learnable Sparse GrowthLi Yang, Sen Lin, Junshan Zhang, Deliang FanPreprint
  6. 2021
    GROWN: GRow Only When Necessary for Continual LearningYang, Li, Sen Lin, Junshan Zhang, Deliang FanarXiv
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  7. 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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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.