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.

6 papers of 8,653Sort Recent · Most cited
  1. 2025
    Visuo-Tactile Class-Incremental LearningHao Fu, Fengyu Yang, Boyang Wang … Hui QianACM Transactions · Zhejiang University · Yale University · +3
  2. 2025
    FM-LoRA: Factorized Low-Rank Meta-Prompting for Continual LearningXiaobing Yu, Jin Yang, Xiao-Ming Wu … Xiaofeng LiuCVPR · Washington University in St. Louis · Yale University
    PDF ↗
  3. 2025
    MultiSFL: Towards Accurate Split Federated Learning via Multi-Model Aggregation and Knowledge ReplayZeke Xia, Ming Hu, Dengke Yan … Mingsong ChenAAAI · East China Normal University · Singapore Management University · +1
    PDF ↗
  4. 2024
    A collective AI via lifelong learning and sharing at the edgeAndrea Soltoggio, Eseoghene Ben-Iwhiwhu, Vladimir Braverman … Soheil KolouriNature Machine Intelligence · Loughborough University · Rice University · +21
  5. 2020
    Compression-aware Continual Learning using Singular Value DecompositionVarigonda Pavan Teja, Priyadarshini PandaarXiv · Yale University
    PDF ↗
  6. 2019
    Visualizing the PHATE of Neural NetworksScott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal MishneNeurIPS · Yale University · Princeton University · +1
    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. 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.