Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

16 papers of 11,817Sort Recent · Most cited
  1. 2026
    Brain-Inspired Synergistic-Memory Prompt Tuning for Few-Shot Class-Incremental LearningJiale Chen, Fenglin Xu, Xin Lyu … Hao TangInformation Fusion
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  4. 2026
    AutoSkill: Experience-Driven Lifelong Learning via Skill Self-EvolutionYu-Tao Yang, Junsong Li, Qianjun Pan … Liang HearXiv
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  5. 2025PDF ↗
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  11. 2024
    COBRA: A Continual Learning Approach to Vision-Brain UnderstandingXuan-Bac Nguyen, Manuel Serna-Aguilera, A. Choudhary … Khoa LuuInternational Journal of Computer Vision
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  12. 2024PDF ↗
  13. 2021
    LIQA: Lifelong Blind Image Quality AssessmentJianzhao Liu, Wei Zhou, Xin Li … Zhibo ChenIEEE Trans. Multimedia · University of Science and Technology of China
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  14. 2021
    Approximating Pareto Optimal Set by An Incremental Learning ModelTingrui Liu, Shenmin Song, Xin Li, Liguo TanIEEE Congress on Evolutionary Computation (CEC) · Harbin Institute of Technology · Shenzhen Institute of Information Technology
  15. 2021
    An Intelligent Transient Stability Assessment Framework With Continual Learning AbilityXin Li, Zeguo Yang, Panfeng Guo, Jiangzhou ChengIEEE TII · China Three Gorges University
  16. 2019
    A Cross‐Dimension Annotations Method for 3D Structural Facial Landmark ExtractionXun Gong, Ping Chen, Zhemin Zhang … Xin LiComputer Graphics Forum · Southwest Jiaotong University · Tsinghua Sichuan Energy Internet Research Institute · +1
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. 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.