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.

5 papers of 8,653Sort 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. 2023
    Few-Shot Continual Learning Based on Vector Symbolic ArchitecturesGeethan Karunaratne, Michael Hersche, Giovanni Cherubini … Abbas RahimiFrontiers · IBM Research - Zurich
  3. 2022
    In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit MemoryGeethan Karunaratne, Michael Hersche, J. Langeneager … Abbas RahimiESSCIRC 2022- IEEE 48th European Solid State Circuits Con… · ETH Zurich · IBM Research - Zurich · +2
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  4. 2022
    Constrained Few-shot Class-incremental LearningMichael Hersche, Geethan Karunaratne, Giovanni Cherubini … Abbas RahimiCVPR · ETH Zurich · IBM Research - Zurich
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  5. 2022
    Introducing principles of synaptic integration in the optimization of deep neural networksGiorgia Dellaferrera, Stanisław Woźniak, Giacomo Indiveri … Evangelos EleftheriouNature Communications · University of Zurich · IBM Research - Zurich · +3
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.