Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 6,984Sort Recent · Most cited
  1. 2021
    Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay BufferJames Smith, Jonathan Balloch, Yen-Chang Hsu, Zsolt KiraIJCNN · Georgia Institute of Technology · Samsung (United States)
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  2. 2021
    Condensed Composite Memory Continual LearningFelix Wiewel, Bin YangIJCNN · University of Stuttgart
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  3. 2021
    Generative Feature Replay with Orthogonal Weight Modification for Continual LearningGehui Shen, Song Zhang, Xiang Chen, Zhihong DengIJCNN · Peking University
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  4. 2021
    Bilevel Continual LearningAmmar Shaker, Francesco Alesiani, Shujian Yu, Wenzhe YinIJCNN · Heidelberg University
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  5. 2021
    Evolutionary NAS in Light of Model Stability for Accurate Continual LearningXiaocong Du, Zheng Li, Jingbo Sun … Yu CaoIJCNN · Arizona State University · Oak Ridge National Laboratory
  6. 2021
    Multi-Modal Meta Continual LearningSibo Gai, Zhengyu Chen, Donglin WangIJCNN · Fudan University · Westlake University
  7. 2021
    Continual Correction of Errors Using Smart Memory ReplayCallum Cory, Diana Benavides‐Prado, Yun Sing KohIJCNN · University of Auckland · Auckland University of Technology
  8. 2021
    A Bayesian approach to Expert Gate Incremental LearningValerio Mieuli, Francesco Ponzio, Alessio Mascolini … Santa Di CataldoIJCNN · Politecnico di Torino
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.