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.

7 papers of 8,653Sort Recent · Most cited
  1. 2024
    SPrint: Self-Paced Continual Learning with Adaptive Curriculum and Memory ReplayMin-Seon Kim, Ling Liu, Hyuk-Yoon KwonIEEE International Conference on Big Data (BigData) · Seoul National University of Science and Technology · Georgia Institute of Technology
  2. 2024
    Natural Mitigation of Catastrophic Interference: Continual Learning in Power-Law Learning EnvironmentsAtith Gandhi, Raj Sanjay Shah, Vijay Marupudi, Sashank VarmaFrontiers · Georgia Institute of Technology
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  3. 2024
    Adaptive Memory Replay for Continual LearningJames Seale Smith, Lazar Valkov, Shaunak Halbe … Leonid KarlinskyCVPR · IBM (United States) · Georgia Institute of Technology
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  4. 2024
    Continual Diffusion with STAMINA: STack-And-Mask INcremental AdaptersJames Seale Smith, Yen-Chang Hsu, Zsolt Kira … Hongxia JinCVPR · Samsung (United States) · Research!America (United States) · +1
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  5. 2024
    NICE: Neurogenesis Inspired Contextual Encoding for Replay-free Class Incremental LearningMustafa Burak Gürbüz, Jean Michael Moorman, Constantine DovrolisCVPR · Georgia Institute of Technology · Cyprus Institute
  6. 2024
    SHARP: Sparsity and Hidden Activation RePlay for Neuro-Inspired Continual LearningMustafa Burak Gürbüz, Jean Michael Moorman, Constantine DovrolisIEEE International Conference on Development and Learning… · Georgia Institute of Technology
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  7. 2024
    Exploring Soft Prompt Initialization Strategy for Few-Shot Continual Text ClassificationZhehao Zhang, Tong Yu, Handong Zhao … Shuai LiICASSP · Dartmouth College · Dartmouth Hospital · +6
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.