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
    Reducing Class-wise Confusion for Incremental Learning with Disentangled ManifoldsHuitong Chen, Yu Wang, Yan Fan … Qinghua HuCVPR · Tianjin University
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  2. 2025
    Multi-View Fusion Graph Attention Network for Multilabel Class Incremental LearningAnhui Tan, Yu Wang, Wei-Zhi Wu … Jiye LiangInformation Fusion · Huaqiao University · Zhejiang Ocean University · +2
  3. 2025
    SPARK: Simple and Parameter-Free Knowledge Embedding With Fuzzy Cognitive Maps for Class Incremental LearningYu Wang, Jiabo Xie, Junyan Zheng … Qinghua HuIEEE Transactions · Tianjin University
  4. 2025
    Compression and restoration: exploring elasticity in continual test-time adaptationJingwei Li, Chengbao Liu, Xiwei Bai … Yu WangMachine Learning · Chinese Academy of Sciences · Shandong Institute of Automation · +2
  5. 2025PDF ↗
  6. 2025
    Self-Updatable Large Language Models by Integrating Context into Model ParametersYu Wang, Xinshuang Liu, Xiusi Chen … Julian McAuleyICLR
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  7. 2025
    Towards LifeSpan Cognitive SystemsYu Wang, Chi Han, Tongtong Wu … Julian McAuleyTMLR
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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.