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.

8 papers of 8,653Sort Recent · Most cited
  1. 2024
    Continuous fake media detection: adapting deepfake detectors to new generative techniquesFrancesco Tassone, Luca Maiano, Irene AmeriniComputer Vision and Image Understanding
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  2. 2024
    Pseudo initialization based Few-Shot Class Incremental LearningMingwen Shao, Xinkai Zhuang, Lixu Zhang, Wangmeng ZuoComputer Vision and Image Understanding · China University of Petroleum, East China · Harbin Institute of Technology
  3. 2023
    Multivariate Prototype Representation for Domain-Generalized Incremental LearningCan Peng, Piotr Koniusz, Kaiyu Guo … Peyman MoghadamComputer Vision and Image Understanding
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  4. 2022
    Continual learning on 3D point clouds with random compressed rehearsalMaciej Zamorski, Michał Stypułkowski, Konrad Karanowski … Maciej ZiębaComputer Vision and Image Understanding
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  5. 2021
    SID: Incremental Learning for Anchor-Free Object Detection via Selective and Inter-Related DistillationCan Peng, Kun Zhao, Sam Maksoud … Brian C. LovellComputer Vision and Image Understanding · The University of Queensland
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  6. 2021
    Knowledge Distillation for Incremental Learning in Semantic SegmentationUmberto Michieli, Pietro ZanuttighComputer Vision and Image Understanding · University of Padua
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  7. 2020
    Deep online classification using pseudo-generative modelsA. G. Besedin, Pierre Blanchart, Michel Crucianu, Marin FerecatuComputer Vision and Image Understanding · Conservatoire National des Arts et Métiers · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · +2
  8. 2016
    Semantic video labeling by developmental visual agentsMarco Gori, Marco Lippi, Marco Maggini, Stefano MelacciComputer Vision and Image Understanding · University of Siena · University of Bologna
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.