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.

10 papers of 8,653Sort Recent · Most cited
  1. 2023
    On the challenges to learn from Natural Data StreamsGuido Borghi, Gabriele Graffieti, Davide MaltoniarXiv
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  2. 2023
    Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual LearningVincenzo Lomonaco, Lorenzo Pellegrini, Gabriele Graffieti, Davide MaltoniarXiv
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  3. 2022
    Continual Learning in Real-Life ApplicationsGabriele Graffieti, Guido Borghi, Davide MaltoniRA-L · University of Bologna
  4. 2023
    Generative Negative Replay for Continual LearningGabriele Graffieti, Davide Maltoni, Lorenzo Pellegrini, Vincenzo LomonacoNeural Networks
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  5. 2022
    Is Class-Incremental Enough for Continual Learning?Andrea Cossu, Gabriele Graffieti, Lorenzo Pellegrini … Vincenzo LomonacoFrontiers · University of Pisa · Scuola Normale Superiore · +1
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  6. 2021
    Avalanche: an End-to-End Library for Continual LearningVincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu … Davide MaltoniCVPR · University of Pisa · University of Bologna · +12
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  7. 2021
    Continual Learning at the Edge: Real-Time Training on Smartphone DevicesLorenzo Pellegrini, Vincenzo Lomonaco, Gabriele Graffieti, Davide MaltoniESANN 2021 proceedings · Azienda-Unita' Sanitaria Locale Di Cesena · University of Pisa · +1
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  8. 2020
    Latent Replay for Real-Time Continual LearningLorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, Davide MaltoniIROS · University of Bologna
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  9. 2020
    IROS 2019 Lifelong Robotic Vision: Object Recognition Challenge [Competitions]Heechul Bae, Eoin Brophy, Rosa H. M. Chan … Liguang ZhouIEEE Robotics & Automation Magazine · Electronics and Telecommunications Research Institute · Dublin City University · +9
  10. 2020PDF ↗
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.