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. 2025
    Distance-based change point detection for novelty detection in concept-agnostic continual anomaly detectionCollin Coil, Kamil Faber, Bartłomiej Śnieżyński, Roberto CorizzoJournal of Intelligent Information Systems · American University · AGH University of Krakow
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  2. 2024
    TinySubNets: An efficient and low capacity continual learning strategyMarcin Pietroń, Kamil Faber, Dominik Żurek, Roberto CorizzoAAAI · AGH University of Krakow · American University
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  3. 2023
    From MNIST to ImageNet and back: benchmarking continual curriculum learningKamil Faber, Dominik Żurek, Marcin Pietroń … Roberto CorizzoMachine Learning · Jagiellonian University · AGH University of Krakow · +2
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  4. 2023
    Ada-QPacknet - Multi-Task Forget-Free Continual Learning with Quantization Driven Adaptive PruningMarcin Pietroń, Dominik Żurek, Kamil Faber, Roberto CorizzoFrontiers · AGH University of Krakow · American University
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  5. 2023
    Transformed-*: A domain-incremental lifelong learning scenario generation frameworkDominik Żurek, Roberto Corizzo, Michał Karwatowski … Kamil FaberIJCNN · Institute of Computer Science · AGH University of Krakow · +1
  6. 2023
    VLAD: Task-agnostic VAE-based lifelong anomaly detectionKamil Faber, Roberto Corizzo, Bartłomiej Śnieżyński, Nathalie JapkowiczNeural Networks · Institute of Computer Science · AGH University of Krakow · +1
  7. 2022
    LIFEWATCH: Lifelong Wasserstein Change Point DetectionKamil Faber, Roberto Corizzo, Bartłomiej Śnieżyński … Nathalie JapkowiczIJCNN · Institute of Computer Science · AGH University of Krakow · +1
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.