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. 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
    GLAM: Efficient Continual Learning at Scale via Grouped LoRA Adapter MergingIrene Testa, L. Quarantiello, E. Coleman … Vincenzo LomonacoarXiv
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  3. 2025
    Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud SegmentationTanuj Sur, Samrat Mukherjee, Kaizer Rahaman … Biplab BanerjeeCVPR
  4. 2025
    Parameter-Efficient Continual Fine-Tuning: A SurveyE. Coleman, L. Quarantiello, Zi-Yue Liu … Vincenzo LomonacoNeurocomputing
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  5. 2025
    UIDAPLE: Unsupervised Incremental Domain Adaptation through Adaptive Prompt LearningSamrat Mukherjee, Tanuj Sur, Saurish Seksaria … Biplab BanerjeeICASSP
  6. 2024
    Deep evolving semi-supervised anomaly detectionJack Belham, Aryan Bhosale, Samrat Mukherjee … Fabio CuzzolinarXiv
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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.