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.

8 papers of 8,653Sort Recent · Most cited
  1. 2026
    Sleep as a system-level resilience mechanism in complex dynamic networks: Insights from biological and artificial systemsL Yang, C T Lin, Haohong Li, Xiaohui WangBrain medicine : · University of Science and Technology of China · Changchun Institute of Applied Chemistry · +1
  2. 2025
    Brain Generative Replay for Continual LearningJian‐Guo Zhou, Dongjun Liu, Wanzeng KongSpringer LNCS · Zhejiang Lab · Hangzhou Dianzi University
  3. 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
  4. 2024
    Few-Shot Class-Incremental Learning via Cross-Modal Alignment with Feature ReplayYanan Li, Linpu He, Feng Lin, Donghui WangSpringer LNCS · Zhejiang University of Science and Technology · Zhejiang Lab
  5. 2024
    Learning Equi-Angular Representations for Online Continual LearningMinhyuk Seo, Hyunseo Koh, Wonje Jeung … Jonghyun ChoiCVPR · Yonsei University · Zhejiang Lab · +1
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  6. 2024
    ESDB: Expand the Shrinking Decision Boundary via One-to-Many Information Matching for Continual Learning With Small MemoryKunchi Li, Hongyang Chen, Jun Wan, Shan YuIEEE TCSVT · Chinese Academy of Sciences · Shandong Institute of Automation · +3
  7. 2023
    Emphasizing Unseen Words: New Vocabulary Acquisition for End-to-End Speech RecognitionLeyuan Qu, Cornelius Weber, Stefan WermterNeural Networks · Universität Hamburg · Zhejiang Lab · +2
    PDF ↗
  8. 2020
    Class incremental learning via Multi-hinge distillationQianhe Lin, Yuanlong Yu, Zhiyong HuangChinese Automation Congress (CAC) · Fuzhou University · Zhejiang Lab
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.