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.

11 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
    Parameter-Efficient Continual Fine-Tuning: A SurveyE. Coleman, L. Quarantiello, Zi-Yue Liu … Vincenzo LomonacoNeurocomputing
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  4. 2024
    Adaptive LoRA Merging for Efficient Domain Incremental LearningL. Quarantiello, E. Coleman, J. Hurtado, Vincenzo LomonacoNeurIPS
  5. 2024
    Continually Learn to Map Visual Concepts to Large Language Models in Resource-constrained EnvironmentsC. Rebillard, J. Hurtado, Andrii Krutsylo … Vincenzo LomonacoNeurocomputing
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  6. 2024
    Adaptive Hyperparameter Optimization for Continual Learning ScenariosRudy Semola, J. Hurtado, Vincenzo Lomonaco, Davide BacciuCLAI Unconf
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  7. 2023
    A Comprehensive Empirical Evaluation on Online Continual LearningAlbin Soutif-Cormerais, Antonio Carta, Andrea Cossu … Hamed HematiICCV
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  8. 2023
    Studying Generalization on Memory-Based Methods in Continual LearningFelipe del Rio, J. Hurtado, Cristian-Radu Buc … Vincenzo LomonacoarXiv
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  9. 2023
    Class-Incremental Learning with RepetitionHamed Hemati, Andrea Cossu, Antonio Carta … Damian BorthCoLLAs
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  10. 2022
    Populating Memory in Continual Learning with Consistency Aware SamplingJ. Hurtado, Alain Raymond-Sáez, Vladimir Araujo … D. BacciuPreprint
  11. 2022
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.