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.

13 papers of 11,817Sort Recent · Most cited
  1. 2026
  2. 2026
    Expandable, Compressible, Mineable: Open-World Thermal Image RestorationPu Li, Huafeng Li, Ya-Fei Zhang … Jie WenarXiv
    PDF ↗
  3. 2026
    Continual Graph Learning with Topology-Aware Knowledge DistillationYu-Ting Zhang, Yanbei Liu, Dongdong Du … Wen WangInternational Conference on Artificial Intelligence and I…
  4. 2026
    FGGM: Fisher-Guided Gradient Masking for Continual LearningChaohong Tan, Qian Chen, Wen Wang … Jieping YeICASSP
    PDF ↗
  5. 2025
    Fun-Audio-Chat Technical ReportQian Chen, Luyao Cheng, Chong Deng … Jing ZhouarXiv
    PDF ↗
  6. 2024
    Incremental Learning via Robust Parameter Posterior FusionWenju Sun, Qingyong Li, Siyu Zhang … Yangli-ao GengACM MM
  7. 2023PDF ↗
  8. 2023
    Class Incremental Learning based on Identically Distributed Parallel One-Class ClassifiersWenju Sun, Qingyong Li, Jing Zhang … Yangli-ao GengNeurocomputing
  9. 2023
  10. 2023
    Exemplar-free class incremental learning via discriminative and comparable parallel one-class classifiersWenju Sun, Qingyong Li, J. Zhang … Yangli-ao GengPattern Recognition
  11. 2023
  12. 2022PDF ↗
  13. 2020
    Deep Inhomogeneous Regularization For Transfer LearningWen Wang, Wei Zhai, Yang CaoICIP · University of Science and Technology of China
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.