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.

7 papers of 6,984Sort Recent · Most cited
  1. 2021
    S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative ReplayHugo Caselles-Dupré, Michael Garcia-Ortiz, David FilliatIEEE International Joint Conference on Neural Network · Institut national de recherche en sciences et technologies du numérique · École Nationale Supérieure de Techniques Avancées · +1
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  2. 2019
    Continual Learning for RoboticsTimothée Lesort, Vincenzo Lomonaco, Andrei Stoian … Natalia Díaz-RodríguezInformation Fusion · Thales (Portugal) · Institut national de recherche en sciences et technologies du numérique · +3
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  3. 2019
    Generative Models from the perspective of Continual LearningTimothée Lesort, Hugo Caselles-Dupré, Michael Garcia-Ortiz … David FilliatIEEE International Joint Conference on Neural Network · Institut national de recherche en sciences et technologies du numérique · École Nationale Supérieure de Techniques Avancées · +2
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  4. 2019
    Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real TransferRené Traoré, Hugo Caselles-Dupré, Timothée Lesort … David FilliatarXiv
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  5. 2019
    Marginal Replay vs Conditional Replay for Continual LearningTimothée Lesort, Alexander Gepperth, Andrei Stoian, David FilliatSpringer LNCS · École Nationale Supérieure de Techniques Avancées · Thales (France) · +1
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  6. 2018
    Don't forget, there is more than forgetting: new metrics for Continual LearningNatalia Díaz-Rodríguez, Vincenzo Lomonaco, David Filliat, Davide MaltoniarXiv · Laboratoire d’Informatique et Systèmes · University of Bologna
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  7. 2018
    Continual State Representation Learning for Reinforcement Learning using Generative ReplayHugo Caselles-Dupré, M. Ortíz, David FilliatarXiv · Laboratoire d’Informatique et Systèmes · SoftBank Robotics (France)
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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.