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. 2025
    Towards Effective Open-set Graph Class-incremental LearningJiazhen Chen, Zheng Ma, Sichao Fu … Weihua OuACM International Conference on Multimedia · University of Waterloo · Huazhong University of Science and Technology · +1
  2. 2024
    Attention-based Vision Knowledge Adaptation for Constrained Continual LearningBicheng Guo, Conghao Zhou, Haoyu Liu … Xuemin ShenGLOBECOM 2024 - 2024 IEEE Global Communications Conference · Zhejiang University · University of Waterloo
  3. 2024
    Memory-Efficient Continual Learning Object Segmentation for Long VideosAmir Nazemi, Mohammad Javad Shafiee, Zahra Gharaee, Paul FieguthIEEE Access · University of Waterloo
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  4. 2023
    Entropy-based Sampling for Streaming learning with Move-to-Data approach on VideoMeghna P. Ayyar, Jenny Benois‐Pineau, Akka Zemmari … Laura E. MiddletonInternational Conference on Content-based Multimedia Inde… · Université de Bordeaux · Laboratoire Bordelais de Recherche en Informatique · +3
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  5. 2023
    CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in RoboticsAli Ayub, Alan R. WagnerIEEE TCDS · University of Waterloo · Concordia University · +1
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  6. 2023
    CLVOS23: A Long Video Object Segmentation Dataset for Continual LearningAmir Nazemi, Zeyad Moustafa, Paul FieguthCVPR · University of Waterloo
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  7. 2021
    Accounting for the Effect of Inter-Task Similarity in Continual Learning Models*Alaa El Khatib, Mahmoud Nasr, Fakhri KarrayIEEE International Conference on Systems, Man, and Cybern… · University of Waterloo
  8. 2020
    CUE: A unified spiking neuron model of short-term and long-term memory.Jan Gosmann, Chris EliasmithPsychological Review · University of Waterloo
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.