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.

6 papers of 11,817Sort Recent · Most cited
  1. 2021
    Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of SubspacesAli Cheraghian, Shafin Rahman, Sameera Ramasinghe … Mehrtash HarandiICCV · Australian National University · Data61 · +2
  2. 2021
    Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental LearningAli Cheraghian, Shafin Rahman, Pengfei Fang … Mehrtash HarandiCVPR · Australian National University · Commonwealth Scientific and Industrial Research Organisation · +3
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  3. 2021
    On Learning the Geodesic Path for Incremental LearningChristian Simon, Piotr Koniusz, Mehrtash HarandiCVPR · Australian National University · Commonwealth Scientific and Industrial Research Organisation · +3
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  4. 2021
    Plastic and Stable Gated Classifiers for Continual LearningNicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier … Hanna SuominenCVPR · Australian National University · UNSW Sydney · +5
  5. 2021
    Learning without Forgetting for 3D Point Cloud ObjectsTownim Faisal Chowdhury, Mahira Jalisha, Ali Cheraghian, Shafin RahmanSpringer LNCS · North South University · Australian National University · +2
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  6. 2021
    Lifelong Explainer for Lifelong LearnersXuelin Situ, Sameen Maruf, Ingrid Zukerman … Gholamreza HaffariEMNLP · Monash University · Commonwealth Scientific and Industrial Research Organisation · +2
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.