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. 2026
    Brain-Inspired Synergistic-Memory Prompt Tuning for Few-Shot Class-Incremental LearningJiale Chen, Fenglin Xu, Xin Lyu … Hao TangInformation Fusion
  2. 2026
    Controlled subspace fusion for language model continual learningXingcan Bao, Jianzhou Feng, Yiru Huo … Jiadong RenInformation Fusion
  3. 2026
    Rethinking domain-agnostic continual learning via frequency completeness learningJian Peng, Haitao Zhang, Jing Shen … Haifeng LiInformation Fusion
  4. 2025
    Multi-View Fusion Graph Attention Network for Multilabel Class Incremental LearningAnhui Tan, Yu Wang, Wei-Zhi Wu … Ji-Ye LiangInformation Fusion
  5. 2024
  6. 2023
    Multi-View Class Incremental LearningDe-Peng Li, Tianqi Wang, Junwei Chen … Zhigang ZengInformation Fusion
    PDF ↗
  7. 2022
    Increasing Depth of Neural Networks for Life-long LearningJędrzej Kozal, Michał WoźniakInformation Fusion · Wrocław University of Science and Technology · AGH University of Krakow
    PDF ↗
  8. 2021
    Non-IID data and Continual Learning processes in Federated Learning: A long road aheadMarcos F. Criado, Fernando E. Casado, Roberto Iglesias … Senén BarroInformation Fusion · Universidade de Santiago de Compostela · Universidade da Coruña
    PDF ↗
  9. 2022
    UIFGAN: An unsupervised continual-learning generative adversarial network for unified image fusionZhuliang Le, Jun Huang, Han Xu … Jiayi MaInformation Fusion · Wuhan University
  10. 2020
  11. 2019
    Continual Learning for RoboticsTimothée Lesort, Vincenzo Lomonaco, Andrei Stoian … Natalia Díaz-RodríguezInformation Fusion · Thales (Portugal) · Institut national de recherche en sciences et technologies du numérique · +3
    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. 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.