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.

5 papers of 8,653Sort Recent · Most cited
  1. 2026
    Continual learning via semantic memory systemMingsen Luo, Qihe Liu, Fei Ye … Shijie ZhouPattern Recognition · University of Electronic Science and Technology of China · University of York
  2. 2026
    Cooperative meta-learning for incremental few-shot object detection in open urban environmentsYuan Li, C F Zhang, Song Yang … Lin WuPattern Recognition · Beijing Institute of Technology · Beijing Electronic Science and Technology Institute · +2
  3. 2025
    TIPS: Two-level prompt selection for more stability-plasticity balance in continual learningZhikun Feng, Liang Peng, Kang Dang … Jionglong SuPattern Recognition · University of Electronic Science and Technology of China · Chengdu University of Information Technology · +3
  4. 2025
    Towards Redundancy-Free Sub-networks in Continual LearningCheng Chen, Lianli Gao, Pengpeng Zeng … Heng Tao ShenPattern Recognition · University of Electronic Science and Technology of China · Tongji University
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  5. 2025
    Online task-free continual learning via Expansible Vision TransformerFei Ye, Adrian G. BorşPattern Recognition · University of Electronic Science and Technology of China · University of York
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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.