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. 2026PDF ↗
  2. 2025
  3. 2025
  4. 2024
  5. 2024
    Cross-Generational Contrastive Continual Learning for 3D Point Cloud Semantic SegmentationYuange He, Guyue Hu, Shan YuInternational Conference on Advances in Brain Inspired Co…
  6. 2023PDF ↗
  7. 2023PDF ↗
  8. 2023PDF ↗
  9. 2022
    RT-Net: replay-and-transfer network for class incremental object detectionBo Cui, Guyue Hu, Shan YuApplied Intelligence · Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · +4
  10. 2022
    Balanced Ranking and Sorting For Class Incremental Object DetectionBo Cui, Hui Qu, Xuhui Huang, Shan YuICASSP · Chinese Academy of Sciences · Institute of Automation · +4
  11. 2021
    Recursive Least-Squares Estimator-Aided Online Learning for Visual TrackingJin Gao, Yan Lu, Xiaojuan Qi … Weiming HuTPAMI · Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · +5
    PDF ↗
  12. 2021
    DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental LearningBo Cui, Guyue Hu, Shan YuAAAI · University of Chinese Academy of Sciences · Center for Excellence in Brain Science and Intelligence Technology · +1
  13. 2018
    Continual learning of context-dependent processing in neural networksGuanxiong Zeng, Yang Chen, Bo Cui, Shan YuNature Machine Intelligence · Chinese Academy of Sciences · Institute of Automation · +2
    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.