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.

11 papers of 11,817Sort Recent · Most cited
  1. 2026PDF ↗
  2. 2026
  3. 2026
  4. 2025
    Rethinking Continual Learning with Pre-Trained Models: Knowledge-Preserving ApproachMakoto Misaizu, Koshi Watanabe, Keisuke Maeda … M. HaseyamaGlobal Conference on Consumer Electronics
  5. 2025
    Analysis of Model Merging for Open-Vocabulary Models with Parameter Efficient Fine-Tuning Leveraging Distributed DataKenta Kubota, Ren Togo, Keisuke Maeda … M. HaseyamaIEEE International Conference on Consumer Electronics
  6. 2025
  7. 2024
    Introducing Class Replacement Technique in Class Incremental Learning in Generative ModelsTaro Togo, Ren Togo, Keisuke Maeda … M. HaseyamaInternational Conference on Consumer Electronics - Taiwan…
  8. 2024
    Analysis of Continual Learning Techniques for Image Generative Models with Learned Class Information ManagementTaro Togo, Ren Togo, Keisuke Maeda … M. HaseyamaItalian National Conference on Sensors
  9. 2024
  10. 2024
    Enhancing Generative Class Incremental Learning Performance With a Model Forgetting ApproachTaro Togo, Ren Togo, Keisuke Maeda … M. HaseyamaIEEE Open Journal of Signal Processing
    PDF ↗
  11. 2023
    Text-to-image Diffusion Model Suppressing Catastrophic Forgetting via Elastic Weight ConsolidationHaruka Matsuda, Ren Togo, Keisuke Maeda … M. HaseyamaGlobal Conference on Consumer Electronics
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.