Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 11,817Sort Recent · Most cited
  1. 2022
    Progressive Latent Replay for efficient Generative RehearsalStanisław Pawlak, Filip Szatkowski, Michał Bortkiewicz … T. P. TrzcinskiInternational Conference on Neural Information Processing
    PDF ↗
  2. 2021
    Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual LearningKamil Rafał Deja, Paweł Wawrzyński, Wojciech Masarczyk … T. P. TrzcinskiIJCAI · Warsaw University of Technology · The University of Texas at Austin · +2
    PDF ↗
  3. 2022
    Continual Learning with Guarantees via Weight Interval ConstraintsMaciej Wołczyk, Karol J. Piczak, Bartosz Wójcik … Przemysław SpurekICML
    PDF ↗
  4. 2022
    Continual learning on 3D point clouds with random compressed rehearsalMaciej Zamorski, Michał Stypułkowski, Konrad Karanowski … Maciej ZiębaComputer Vision and Image Understanding
    PDF ↗
  5. 2022
    Logarithmic Continual LearningWojciech Masarczyk, Paweł Wawrzyński, Daniel Marczak … T. P. TrzcinskiIEEE Access · Warsaw University of Technology · Jagiellonian University
    PDF ↗
  6. 2020
    BinPlay: A Binary Latent Autoencoder for Generative Replay Continual LearningKamil Rafał Deja, Paweł Wawrzyński, Daniel Marczak … T. P. TrzcinskiIEEE International Joint Conference on Neural Network · Warsaw University of Technology
    PDF ↗
  7. 2021
    On robustness of generative representations against catastrophic forgettingWojciech Masarczyk, Kamil Rafał Deja, T. P. TrzcinskiSpringer CCIS · Warsaw University of Technology · Jagiellonian University
    PDF ↗
  8. 2021
    Continual Learning of 3D Point Cloud GeneratorsMichał Sadowski, Karol J. Piczak, Przemysław Spurek, T. P. TrzcinskiSpringer LNCS · Jagiellonian University · Warsaw University of Technology
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 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.