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.

8 papers of 8,653Sort Recent · Most cited
  1. 2025
    Exploring the Stability Gap in Continual Learning: The Role of the Classification HeadWojciech Łapacz, Daniel Marczak, Filip Szatkowski, T. P. TrzcinskiWACV · Warsaw University of Technology
    PDF ↗
  2. 2025
    Variance-Covariance Regularization Improves Continual LearningPiotr Hondra, Daniel Marczak, Kamil Rafał DejaIEEE Access · Warsaw University of Technology
  3. 2024
    Category Adaptation Meets Projected Distillation in Generalized Continual Category DiscoveryGrzegorz Rypeść, Daniel Marczak, Sebastian Cygert … Bartłomiej TwardowskiECCV · Warsaw University of Technology · Gdańsk University of Technology · +1
    PDF ↗
  4. 2024
    MagMax: Leveraging Model Merging for Seamless Continual LearningDaniel Marczak, Bartłomiej Twardowski, T. P. Trzcinski, Sebastian CygertECCV
    PDF ↗
  5. 2024
    Revisiting Supervision for Continual Representation LearningDaniel Marczak, Sebastian Cygert, T. P. Trzcinski, Bartłomiej TwardowskiECCV
    PDF ↗
  6. 2022
    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 ↗
  7. 2022
    Logarithmic Continual LearningWojciech Masarczyk, Paweł Wawrzyński, Daniel Marczak … T. P. TrzcinskiIEEE Access · Warsaw University of Technology · Jagiellonian University
    PDF ↗
  8. 2021
    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 ↗
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.