Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 8,653Sort Recent · Most cited
  1. 2025
    Parameter-Efficient Continual Fine-Tuning: A SurveyE N Coleman, Luigi Quarantiello, Ziyue Liu … Vincenzo LomonacoNeurocomputing · University of Pisa · Politecnico di Torino · +4
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  2. 2026
    Cooperative meta-learning for incremental few-shot object detection in open urban environmentsYuan Li, C F Zhang, Song Yang … Lin WuPattern Recognition · Beijing Institute of Technology · Beijing Electronic Science and Technology Institute · +2
  3. 2024
    Continually Learn to Map Visual Concepts to Large Language Models in Resource-constrained EnvironmentsClea Rebillard, Julio Hurtado, Andrii Krutsylo … Vincenzo LomonacoNeurocomputing · Institut Polytechnique de Bordeaux · University of Warwick · +3
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  4. 2024
    Adaptive LoRA Merging for Efficient Domain Incremental LearningLuigi Quarantiello, Eric Coleman, Julio Hurtado, Vincenzo LomonacoNeurIPS · University of Warwick
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  5. 2022
    Class Similarity Weighted Knowledge Distillation for Continual Semantic SegmentationVu Minh Hieu Phan, The-Anh Ta, Son Lam Phung … Abdesselam BouzerdoumCVPR · University of Wollongong · FPT University · +2
  6. 2020
    From eye-blinks to state construction: Diagnostic benchmarks for online representation learningBanafsheh Rafiee, Zaheer Abbas, Sina Ghiassian … Adam WhiteAdaptive Behavior · University of Alberta · University of Warwick
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  7. 2020
    Optimal Continual Learning has Perfect Memory and is NP-hardJeremias Knoblauch, Hisham Husain, Tom DietheICML · University of Warwick · The Alan Turing Institute · +2
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  8. 2002
    Using Noise to Compute Error Surfaces in Connectionist Networks: A Novel Means of Reducing Catastrophic ForgettingRobert M. French, Nick ChaterNeural Computation · University of Liège · University of Warwick
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.