Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 6,984Sort Recent · Most cited
  1. 2025
    CKDF-V2: Effectively Alleviating Representation Shift for Continual Learning With Small MemoryKunchi Li, Hongyang Chen, Jun Wan, Shan YuTNNLS · Xiamen University of Technology · Zhejiang Lab · +2
  2. 2025
    Continual Learning With Knowledge Distillation: A SurveySongze Li, Tonghua Su, Xu-Yao Zhang, Zhongjie WangTNNLS · Harbin Institute of Technology · Chinese Academy of Sciences · +1
  3. 2025
    Brain-Inspired Fast- and Slow-Update Prompt Tuning for Few-Shot Class-Incremental LearningHang Ran, Xingyu Gao, Lusi Li … Xin NingTNNLS · Chinese Academy of Sciences · Institute of Semiconductors · +3
  4. 2024
    Online Active Continual Learning for Robotic Lifelong Object RecognitionXiangli Nie, Zhiguang Deng, Mingdong He … Zheng TangTNNLS · Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · +5
  5. 2023
    InOR-Net: Incremental 3-D Object Recognition Network for Point Cloud RepresentationJiahua Dong, Yang Cong, Gan Sun … Ender KonukoğluTNNLS · Shenyang Institute of Automation · Chinese Academy of Sciences · +4
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  6. 2019
    Representative Task Self-Selection for Flexible Clustered Lifelong LearningGan Sun, Yang Cong, Qianqian Wang … Yun FuTNNLS · Northeastern University · Shenyang Institute of Automation · +3
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  7. 2021
    Incremental Concept Learning via Online Generative Memory RecallHuaiyu Li, Weiming Dong, Bao-Gang HuTNNLS · Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · +2
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  8. 2021
    Continual Multiview Task Learning via Deep Matrix FactorizationGan Sun, Yang Cong, Yulun Zhang … Yun FuTNNLS · Northeastern University · Shenyang Institute of Automation · +2
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.