Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

6 papers of 6,984Sort Recent · Most cited
  1. 2021
    Novelty detection for unsupervised continual learning in image sequencesRuiqi Dai, Mathieu Lefort, Frédéric Armetta … Stefan DuffnerIEEE 33rd International Conference on Tools with Artifici… · Lyon 1 Université · Centre National de la Recherche Scientifique · +3
  2. 2021
    Memory Efficient Invertible Neural Networks for Class-Incremental LearningGuillaume Hocquet, Olivier Bichler, Damien QuerliozIEEE 3rd International Conference on Artificial Intellige… · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Intégration des Systèmes et des Technologies · +2
  3. 2021
    Synaptic metaplasticity in binarized neural networksAxel Laborieux, Maxence Ernoult, Tifenn Hirtzlin, Damien QuerliozNature Communications · Centre National de la Recherche Scientifique · Université Paris-Saclay · +2
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  4. 2021
    Semi-Supervised Class Incremental LearningAlexis Lechat, Stéphane Herbin, Frédéric JurieICPR · Centre National de la Recherche Scientifique · École Nationale Supérieure d'Ingénieurs de Caen · +5
  5. 2021
    Studying Catastrophic Forgetting in Neural Ranking ModelsJesús Lovón-Melgarejo, Laure Soulier, Karen Pinel-Sauvagnat, Lynda TamineSpringer LNCS · Université Toulouse III - Paul Sabatier · Institut de Recherche en Informatique de Toulouse · +4
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  6. 2021
    Self-supervised Continual Learning for Object Recognition in Image SequencesRuiqi Dai, Mathieu Lefort, Frédéric Armetta … Stefan DuffnerSpringer CCIS · Lyon 1 Université · Centre National de la Recherche Scientifique · +3
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.