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. 2022
    Power Law in Deep Neural Networks: Sparse Network Generation and Continual Learning With Preferential AttachmentFan Feng, Lu Hou, Qi She … James T. KwokTNNLS · City University of Hong Kong · Hong Kong University of Science and Technology
  2. 2022
    Camel: Managing Data for Efficient Stream LearningYiming Li, Yanyan Shen, Lei Chen2022 International Conference on Management of Data · Hong Kong University of Science and Technology · Shanghai Jiao Tong University
  3. 2022
    Representation Compensation Networks for Continual Semantic SegmentationChang–Bin Zhang, Jia-wen Xiao, Xialei Liu … Ming‐Ming ChengCVPR · Nankai University · Hong Kong University of Science and Technology
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  4. 2022
    Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismBinbin Yang, Xinchi Deng, Shi Han … Xiaodan LiangCVPR · Sun Yat-sen University · Hong Kong University of Science and Technology · +1
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  5. 2022
    Continual Attentive Fusion for Incremental Learning in Semantic SegmentationGuanglei Yang, Enrico Fini, Dan Xu … Elisa RicciIEEE Trans. Multimedia · University of Trento · Harbin Institute of Technology · +5
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  6. 2022
    Uncertainty-aware Contrastive Distillation for Incremental Semantic SegmentationGuanglei Yang, Enrico Fini, Dan Xu … Elisa RicciTPAMI · Harbin Institute of Technology · University of Trento · +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. 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.