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. 2024
    Improving Plasticity in Online Continual Learning via Collaborative LearningMaorong Wang, Nicolas Michel, Ling Xiao, Toshihiko YamasakiCVPR · The University of Tokyo · Université Gustave Eiffel
    PDF ↗
  2. 2024
    Learning Representations on the Unit Sphere: Investigating Angular Gaussian and Von Mises-Fisher Distributions for Online Continual LearningNicolas Michel, Giovanni Chierchia, Romain Negrel, Jean‐François BercherAAAI · Centre National de la Recherche Scientifique · Laboratoire d'Informatique Gaspard-Monge · +1
    PDF ↗
  3. 2023
    New metrics for analyzing continual learnersNicolas Michel, Giovanni Chierchia, Romain Negrel … Toshihiko YamasakiarXiv · Laboratoire d'Informatique Gaspard-Monge · Université Gustave Eiffel
    PDF ↗
  4. 2022
    Contrastive Learning for Online Semi-Supervised General Continual LearningNicolas Michel, Romain Negrel, Giovanni Chierchia, Jean‐François BercherICIP · Centre National de la Recherche Scientifique · Laboratoire d'Informatique Gaspard-Monge · +1
    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. 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.