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. 2019
    Complementary Learning for Overcoming Catastrophic Forgetting Using Experience ReplayMohammad Rostami, Soheil Kolouri, Praveen K. PillyIJCAI · California University of Pennsylvania · University of Pennsylvania · +1
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  2. 2019
    Closed-Loop Memory GAN for Continual LearningAmanda Rios, Laurent IttiIJCAI · University of Southern California · California Southern University
  3. 2019
    Learning Shared Knowledge for Deep Lifelong Learning using Deconvolutional NetworksSeungwon Lee, James Stokes, Eric EatonIJCAI · University of Pennsylvania · Flatiron Health (United States) · +1
  4. 2019
    Extensible Cross-Modal HashingTianyi Chen, Lan Zhang, Shi-cong Zhang … Bai-chuan HuangIJCAI · University of Science and Technology of China · Northeastern University · +1
  5. 2019
    Self-Organizing Incremental Neural Networks for Continual LearningChayut Wiwatcharakoses, Daniel BerrarIJCAI · Tokyo Institute of Technology
  6. 2019
    Towards AutoML in the presence of Drift: first resultsJorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales … Michèle SébagIJCAI · National Institute of Astrophysics, Optics and Electronics · Nanjing University · +2
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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.