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.

7 papers of 11,817Sort Recent · Most cited
  1. 2022
    Prototypical quadruplet for few-shot class incremental learningSanchar Palit, Biplab Banerjee, Subhasis ChaudhuriProcedia Computer Science · Indian Institute of Technology Bombay
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  2. 2022
    Semantics-Driven Generative Replay for Few-Shot Class Incremental LearningAishwarya Agarwal, Biplab Banerjee, Fabio Cuzzolin, Subhasis ChaudhuriACM International Conference on Multimedia · Indian Institute of Technology Bombay · Oxford Brookes University
  3. 2019
    Neural Program Induction for KBQA Without Gold Programs or Query AnnotationsGhulam Ahmed Ansari, Amrita Saha, Vishwajeet Kumar … Soumen ChakrabartiIJCAI · IBM Research - India · Indian Institute of Technology Bombay
  4. 2019
    Unsupervised incremental learning for hand shape and pose estimationPratik Kalshetti, Parag ChaudhuriACM SIGGRAPH 2019 Posters · Indian Institute of Technology Bombay
  5. 2019
    Continual Learning with Neural Networks: A ReviewAbhijeet Awasthi, Sunita SarawagiACM India Joint International Conference on Data Science… · Indian Institute of Technology Bombay
  6. 2017
    Labeled Memory Networks for Online Model AdaptationShiv Shankar, Sunita SarawagiAAAI · Indian Institute of Technology Bombay
  7. 2018
    HOUDINI: Lifelong Learning as Program SynthesisLazar Valkov, Dipak Chaudhari, Akash Srivastava … Swarat ChaudhuriNeurIPS · Indian Institute of Technology Bombay · IBM (United States) · +2
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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 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.