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.

4 papers of 8,653Sort Recent · Most cited
  1. 2009
    Online Incremental Face Recognition System Using Eigenface Feature and Neural ClassifierSeiichi Ozawa, Shigeo Abe, Shaoning Pang, Nikola KasabovState of the Art in Face Recognition
  2. 2002
    Reducing computations in incremental learning for feedforward neural network with long-term memoryM. Kobyashi, Abu Sarwar Zamani, Seiichi Ozawa, Shigeo AbeIJCNN · Kobe University
  3. 2002
    A reinforcement learning algorithm for neural networks with incremental learning abilityN. Shiraga, Seiichi Ozawa, Shigeo AbeInternational Conference on Neural Information Processing… · Kobe University
  4. 2002
    Incremental Learning Algorithm for Feedforward Neural Network with Long-Term MemoryMasataka KOBAYASHI, Seiichi Ozawa, Shigeo AbeTransactions of the Society of Instrument and Control Eng… · Kobe University
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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.