Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

7 papers of 6,984Sort Recent · Most cited
  1. 2024
    Balancing Continual Learning and Fine-tuning for Human Activity RecognitionChi Ian Tang, Lorena Qendro, Dimitris Spathis … Cecilia MascoloarXiv
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  2. 2024
    Kaizen: Practical self-supervised continual learning with continual fine-tuningChi Ian Tang, Lorena Qendro, Dimitris Spathis … Akhil MathurWACV · University of Cambridge · Nokia (United Kingdom)
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  3. 2023
    LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing PlatformsYoung D. Kwon, Jagmohan Chauhan, Hong Jia … Cecilia MascoloACM International Conference on Embedded Networked Sensor…
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  4. 2022PDF ↗
  5. 2021
    Exploring System Performance of Continual Learning for Mobile and Embedded Sensing ApplicationsYoung D. Kwon, Jagmohan Chauhan, Abhishek Kumar … Cecilia MascoloTyöväentutkimus Vuosikirja · University of Cambridge · University of Southampton
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  6. 2021
    FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing ApplicationsYoung D. Kwon, Jagmohan Chauhan, Cecilia MascoloInterspeech · University of Cambridge · University of Southampton
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  7. 2021
    Knowing when we do not know: Bayesian continual learning for sensing-based analysis tasksSandra Servia-Rodríguez, Cecilia Mascolo, Young D. KwonarXiv
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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. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.