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.

57 papers of 8,653 · showing 51–57Sort Recent · Most cited
  1. 2022
    Incremental Learning from Low-labelled Stream Data in Open-Set Video Face RecognitionEric López‐López, Xosé M. Pardo, Carlos V. RegueiroPattern Recognition · Universidade da Coruña · Universidade de Santiago de Compostela
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  2. 2022
    Multi-View Correlation Distillation for Incremental Object DetectionDongbao Yang, Yu Zhou, Aoting Zhang … Qixiang YePattern Recognition · Chinese Academy of Sciences · Institute of Information Engineering · +1
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  3. 2022
    Instance exploitation for learning temporary concepts from sparsely labeled drifting data streamsŁukasz Korycki, Bartosz KrawczykPattern Recognition · Virginia Commonwealth University
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  4. 2021
    Lifelong robotic visual-tactile perception learningJiahua Dong, Yang Cong, Gan Sun, Tao ZhangPattern Recognition · Shenyang Institute of Automation · Chinese Academy of Sciences · +1
  5. 2021
    FoCL: Feature-Oriented Continual Learning for Generative ModelsQicheng Lao, Mehrzad Mortazavi, Marzieh S. Tahaei … Mohammad HavaeiPattern Recognition · Sichuan University · West China Medical Center of Sichuan University · +3
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  6. 2018
    A novel random forests based class incremental learning method for activity recognitionChunyu Hu, Yiqiang Chen, Lisha Hu, Xiaohui PengPattern Recognition · Chinese Academy of Sciences · Institute of Computing Technology · +2
  7. 2011
    Evolution of heterogeneous ensembles through dynamic particle swarm optimization for video-based face recognitionJean-François Connolly, Éric Granger, Robert SabourinPattern Recognition · Université du Québec à Montréal · École de Technologie Supérieure
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.