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.

4 papers of 8,653Sort 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
    Enhance the old representations' adaptability dynamically for exemplar-free continual learningKunchi Li, Chaoyue Ding, Jun Wan, Shan YuNeurocomputing · Shandong Institute of Automation · Institute of Automation · +1
  3. 2025
    Collaborative Adapter Experts for Class-Incremental LearningSunyuan Qiang, Xinxing Yu, Yanyan Liang … Du ZhangIEEE Signal Processing Letters · Macau University of Science and Technology · Chinese Academy of Sciences · +1
  4. 2025
    Dual Balanced Class-Incremental Learning With im-Softmax and Angular RectificationRuicong Zhi, Yicheng Meng, Junyi Hou, Jun WanTNNLS · University of Science and Technology Beijing · National University of Singapore · +4
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.