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.

10 papers of 11,817Sort Recent · Most cited
  1. 2026
    \textsc{PGO-BEN}: Proxy-Guided Orthogonalization and Beta Ensembling for Few-Shot Domain-Incremental LearningSamrat Mukherjee, T. Venkateswaran, E. Coleman … Biplab BanerjeeTrans. Mach. Learn. Res.
  2. 2025
    Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud SegmentationTanuj Sur, Samrat Mukherjee, Kaizer Rahaman … Biplab BanerjeeCVPR
  3. 2025
    UIDAPLE: Unsupervised Incremental Domain Adaptation through Adaptive Prompt LearningSamrat Mukherjee, Tanuj Sur, Saurish Seksaria … Biplab BanerjeeICASSP
  4. 2024
    Deep evolving semi-supervised anomaly detectionJack Belham, Aryan Bhosale, Samrat Mukherjee … Fabio CuzzolinarXiv
    PDF ↗
  5. 2024
    Revised Regularization for Efficient Continual Learning through Correlation-Based Parameter Update in Bayesian Neural Networks✱Sanchar Palit, Biplab Banerjee, Subhasis ChaudhuriIndian Conference on Computer Vision, Graphics & Image Pr…
    PDF ↗
  6. 2024
    Incremental Open-set Domain AdaptationSayan Rakshit, Hmrishav Bandyopadhyay, Nibaran Das, Biplab BanerjeearXiv
    PDF ↗
  7. 2024
    Few Shot Class Incremental Learning using Vision-Language modelsAnurag Kumar, Chinmay Bharti, Saikat Dutta … Biplab BanerjeearXiv
    PDF ↗
  8. 2022
    Prototypical quadruplet for few-shot class incremental learningSanchar Palit, Biplab Banerjee, Subhasis ChaudhuriProcedia Computer Science · Indian Institute of Technology Bombay
    PDF ↗
  9. 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
  10. 2021
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.