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.

5 papers of 8,653Sort Recent · Most cited
  1. 2023
    FedViT: Federated continual learning of vision transformer at edgeXiaojiang Zuo, Yaxin Luopan, Rui Han … Lydia Y. ChenFuture Generation Computer Systems · Beijing Institute of Technology · Delft University of Technology
  2. 2023
    Evaluating Differential Privacy in Federated Continual LearningJunyan Ouyang, Rui Han, Chi Harold LiuIEEE 98th Vehicular Technology Conference (VTC2023-Fall) · Beijing Institute of Technology
  3. 2023
    Exploring Data Geometry for Continual LearningZhi Gao, Chen Xu, Feng Li … Yuwei WuCVPR · Beijing Institute of Technology · Shenzhen University · +3
    PDF ↗
  4. 2023
    FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at EdgeYaxin Luopan, Rui Han, Qinglong Zhang … Lydia Y. ChenICDE · Beijing Institute of Technology · Beijing Research Institute of Mechanical and Electrical Technology · +1
    PDF ↗
  5. 2023
    Class Incremental Learning with Important and Diverse MemoryLi Mei, Zeyu Yan, Changsheng LiSpringer LNCS · Beijing Institute of Technology
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.