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. 2024
    APM: Adaptive parameter multiplexing for class incremental learningJinghan Gao, Tao Xie, Ruifeng Li … Li-Jun ZhaoExpert Systems with Applications · Harbin Institute of Technology
  2. 2024
    Revisiting class-incremental object detection: An efficient approach via intrinsic characteristics alignment and task decouplingLiang Bai, Hong Song, Tao Feng … Jian YangExpert Systems with Applications · Beijing Institute of Technology · Tsinghua University
  3. 2024
    A flexible enhanced fuzzy min-max neural network for pattern classificationEssam Alhroob, Mohammed Falah Mohammed, Osama Nayel Al Sayaydeh … Chee Peng LimExpert Systems with Applications · Isra University · University of Mosul · +4
  4. 2024
    Rethinking few-shot class-incremental learning: A lazy learning baselineZhili Qin, Wei Han, Jiaming Liu … Junming ShaoExpert Systems with Applications · University of Electronic Science and Technology of China · Quzhou University · +1
  5. 2024
    Class-incremental learning with causal relational replayToan Nguyen, Duc Kieu, Bao Duong … Bac LeExpert Systems with Applications · Vietnam National University Ho Chi Minh City · Deakin University · +2
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.