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.

9 papers of 8,653Sort Recent · Most cited
  1. 2026
    SPREAD: Subspace Representation Distillation for Lifelong Imitation LearningKaushik Roy, Giovanni D'urso, Nicholas Lawrance … Peyman MoghadamarXiv
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  2. 2025
    Flashbacks to Harmonize Stability and Plasticity in Continual LearningLeila Mahmoodi, Peyman Moghadam, Munawar Hayat … Mehrtash HarandiNeural Networks · Monash University · Commonwealth Scientific and Industrial Research Organisation · +3
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  3. 2025
    Inductive Graph Few-shot Class Incremental LearningYayong Li, Peyman Moghadam, Can Peng … Piotr KoniuszWSDM · The University of Queensland · Commonwealth Scientific and Industrial Research Organisation · +2
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  4. 2023
    CL3: Generalization of Contrastive Loss for Lifelong LearningKaushik Roy, Christian Simon, Peyman Moghadam, Mehrtash HarandiJournal of Imaging · Commonwealth Scientific and Industrial Research Organisation · Data61 · +3
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  5. 2023
    Flashback for Continual LearningLeila Mahmoodi, Mehrtash Harandi, Peyman MoghadamICCV · Commonwealth Scientific and Industrial Research Organisation · Australian Regenerative Medicine Institute · +3
  6. 2023
    Multivariate Prototype Representation for Domain-Generalized Incremental LearningCan Peng, Piotr Koniusz, Kaiyu Guo … Peyman MoghadamComputer Vision and Image Understanding
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  7. 2023
    Subspace Distillation for Continual LearningKaushik Roy, Christian Simon, Peyman Moghadam, Mehrtash HarandiNeural Networks
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  8. 2023
    L3DMC: Lifelong Learning using Distillation via Mixed-Curvature SpaceKaushik Roy, Peyman Moghadam, Mehrtash HarandiMICCAI
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  9. 2022
    InCloud: Incremental Learning for Point Cloud Place RecognitionJoshua Knights, Peyman Moghadam, Milad Ramezani … Clinton FookesIROS · Commonwealth Scientific and Industrial Research Organisation · Queensland University of Technology · +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. 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.