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
    Transfer and merge: a feature adaptation method for non-exemplar class-incremental learningTianqi Kong, Yuefeng Sun, Fengna ChengMultimedia Systems · Nanjing University of Information Science and Technology · Nanjing Forestry University
  2. 2025
    DiLien: Domain-Incremental Low-Light Image EnhancementXingxin Zou, Haozhe Zhang, Fengna ChengIEEE Multimedia · Nanjing University of Information Science and Technology · Nanjing Forestry University
  3. 2025
    DuPt: Rehearsal-based continual learning with dual promptsShengqin Jiang, Daolong Zhang, Fengna Cheng … Qingshan LiuNeural Networks · Nanjing University of Information Science and Technology · Nanjing Forestry University · +2
  4. 2025
    Self-Reflection Neural Network for Class-Incremental Object CountingShengqin Jiang, Linfei Li, Fengna Cheng … Qingshan LiuIEEE Trans. Multimedia · Nanjing University of Information Science and Technology · Nanjing Forestry University · +2
  5. 2022
    A Unified Object Counting Network With Object Occupation PriorShengqin Jiang, Qing Wang, Fengna Cheng … Qingshan LiuIEEE TCSVT · Nanjing University of Information Science and Technology · Nanjing Forestry University · +3
    PDF ↗
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 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.