Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 11,817Sort Recent · Most cited
  1. 2021
    Generative feature-driven image replay for continual learningKevin Thandiackal, Tiziano Portenier, Andrea Giovannini … Orçun GökselImage and Vision Computing · ETH Zurich · IBM Research - Zurich · +1
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  2. 2021
    Continual Adaptation of Semantic Segmentation Using Complementary 2D-3D Data RepresentationsJonas Frey, Hermann Blum, Francesco Milano … César CadenaRA-L · ETH Zurich
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  3. 2021
    A TinyML Platform for On-Device Continual Learning With Quantized Latent ReplaysLeonardo Ravaglia, Manuele Rusci, Davide Nadalini … Luca BeniniIEEE Journal on Emerging and Selected Topics in Circuits… · University of Bologna · University of Modena and Reggio Emilia · +1
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  4. 2021
    EILE: Efficient Incremental Learning on the EdgeXi Chen, Chang Gao, Tobi Delbrück, Shih‐Chii LiuIEEE 3rd International Conference on Artificial Intellige… · SIB Swiss Institute of Bioinformatics · University of Zurich · +1
  5. 2021
    Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemmaSimon Schug, Frederik Benzing, Angelika StegerbioRxiv · SIB Swiss Institute of Bioinformatics · University of Zurich · +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.