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.

13 papers of 8,653Sort Recent · Most cited
  1. 2026PDF ↗
  2. 2026PDF ↗
  3. 2026
    MePo: Meta Post-Refinement for Rehearsal-Free General Continual LearningGuanglong Sun, Hongwei Yan, Liyuan Wang … Yi ZhongICML
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  4. 2026PDF ↗
  5. 2025
    Domain Generalizable Continual LearningHongwei Yan, Guanglong Sun, Zhiqi Kang … Liyuan WangarXiv
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  6. 2024
    Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-DistillationHongwei Yan, Liyuan Wang, Kaisheng Ma, Yi ZhongCVPR · McGovern Institute for Brain Research · King Center · +1
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  7. 2024
    On-Chip Incremental Learning based on Unsupervised STDP ImplementationGuang Chen, Jian Cao, Shuo Feng … Yuan WangIEEE 6th International Conference on AI Circuits and Syst… · Peking University
  8. 2023
    Incorporating neuro-inspired adaptability for continual learning in artificial intelligenceLiyuan Wang, Xingxing Zhang, Qian Li … Yi ZhongNature Machine Intelligence · Chinese Institute for Brain Research · Center for Life Sciences · +2
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  9. 2022
    CoSCL: Cooperation of Small Continual Learners is Stronger than a Big OneLiyuan Wang, Xingxing Zhang, Qian Li … Yi ZhongECCV
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  10. 2022
    Memory Replay with Data Compression for Continual LearningLiyuan Wang, Xingxing Zhang, Kuo Yang … Jun ZhuICLR
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  11. 2021
    AFEC: Active Forgetting of Negative Transfer in Continual LearningLiyuan Wang, Ming‐Tian Zhang, Zhongfan Jia … Yi ZhongNeurIPS · Tsinghua University · University College London
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  12. 2022
    Triple-Memory Networks: A Brain-Inspired Method for Continual LearningLiyuan Wang, Bo Lei, Qian Li … Yi ZhongTNNLS · Chinese Institute for Brain Research · Center for Life Sciences · +1
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  13. 2021
    Few-shot Continual Learning: a Brain-inspired ApproachLiyuan Wang, Qian Li, Yi Zhong, Jun ZhuarXiv
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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.