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.

5 papers of 11,817Sort Recent · Most cited
  1. 2017
    Incremental Boosting Convolutional Neural Network for Facial Action Unit RecognitionShizhong Han, Zibo Meng, Ahmed Shehab Khan, Yan TongNeurIPS
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  2. 2017
    Gradient Episodic Memory for Continual LearningDavid López-Paz, Marc’Aurelio RanzatoNeurIPS · Max Planck Society · Max Planck Innovation · +1
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  3. 2017
    Continual Learning with Deep Generative ReplayHanul Shin, Jung Kwon Lee, Jaehong Kim, Jiwon KimNeurIPS · Seoul National University · Samsung (South Korea)
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  4. 2017
    Streaming Sparse Gaussian Process ApproximationsThang D. Bui, Cuong V. Nguyen, Richard E. TurnerNeurIPS · University of Cambridge
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  5. 2017
    Overcoming Catastrophic Forgetting by Incremental Moment MatchingSang-Woo Lee, Jin-Hwa Kim, Jae-Hyun Jun … Byoung‐Tak ZhangNeurIPS · Seoul National University · Naver (South Korea)
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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. 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.