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. 2026
    Mitigating Catastrophic Forgetting in Multilingual Continual Pretraining: Lessons from EU Institutional LLMsCarolina Oliveira Costa, Bhavani Bhaskar, Hans Ewetz … Ilja RauschProcedia Computer Science · European Commission · Directorate General for Communications Networks, Content and Technology
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  2. 2023
    Prototypical quadruplet for few-shot class incremental learningSanchar Palit, Biplab Banerjee, Subhasis ChaudhuriProcedia Computer Science · Indian Institute of Technology Bombay
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  3. 2018
    Investigation of Incremental Learning as Temporal Feature ExtractionShoya Matsumori, Yuki Abe, Masahiko Osawa, Michita ImaiProcedia Computer Science · Keio University · Japan Society for the Promotion of Science
  4. 2018
    Lifelong Learning with the Feedback-loop between Emotions and Actions via Internal RewardDharani Punithan, Byoung‐Tak ZhangProcedia Computer Science · Seoul National University
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.