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.

9 papers of 8,653Sort Recent · Most cited
  1. 2022
    Continual Learning with Invertible Generative ModelsJary Pomponi, Simone Scardapane, Aurelio UnciniNeural Networks · Sapienza University of Rome
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  2. 2022
    Growing dendrites enhance a neuron's computational power and memory capacityWilliam B. Levy, Robert A. BaxterNeural Networks · University of Virginia · Baxter (United States)
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  3. 2022
    Continual learning with attentive recurrent neural networks for temporal data classificationShao-Yu Yin, Yu Huang, Tien-Yu Chang … Vincent S. TsengNeural Networks · National Yang Ming Chiao Tung University · Industrial Technology Research Institute · +1
  4. 2022
    Efficient Perturbation Inference and Expandable Network for continual learningFei Du, Yun Yang, Ziyuan Zhao, Zeng ZengNeural Networks · Yunnan University · Agency for Science, Technology and Research · +1
  5. 2022
    CLAD: A realistic Continual Learning benchmark for Autonomous DrivingEli Verwimp, Kuo Yang, Sarah Parisot … Tinne TuytelaarsNeural Networks
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  6. 2022
    Return of the normal distribution: Flexible deep continual learning with variational auto-encodersYongwon Hong, Martin Mundt, Sungho Park … Hyeran ByunNeural Networks · Yonsei University · Technische Universität Darmstadt · +1
  7. 2022
    Continual Object Detection: A review of definitions, strategies, and challengesAngelo Garangau Menezes, Gustavo de Moura, Cézanne Alves, André C. P. L. F. de CarvalhoNeural Networks
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  8. 2022
    Continual Pre-Training Mitigates Forgetting in Language and VisionAndrea Cossu, Tinne Tuytelaars, Antonio Carta … Davide BacciuNeural Networks
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  9. 2022
    Generative Negative Replay for Continual LearningGabriele Graffieti, Davide Maltoni, Lorenzo Pellegrini, Vincenzo LomonacoNeural Networks
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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.