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.

20 papers of 11,817Sort Recent · Most cited
  1. 2024
    Simplified priors for Object-Centric LearningVihang Patil, A. Radler, Daniel Klotz, Sepp HochreiterCoLLAs
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  4. 2024
    Learning to Learn without Forgetting using AttentionAnna Vettoruzzo, Joaquin Vanschoren, Mohamed-Rafik Bouguelia, Thorsteinn S. RögnvaldssonCoLLAs
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  5. 2024
    Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer LearningNorman Di Palo, Leonard Hasenclever, Jan Humplik, Arunkumar ByravanCoLLAs
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  6. 2024
    Local vs Global continual learningGiulia Lanzillotta, Sidak Pal Singh, B. Grewe, Thomas HofmannCoLLAs
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  7. 2024
    Reflecting on the State of Rehearsal-free Continual Learning with Pretrained ModelsLukas Thede, Karsten Roth, Olivier J. H'enaff … Zeynep AkataCoLLAs
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  8. 2024PDF ↗
  9. 2024
    Statistical Context Detection for Deep Lifelong Reinforcement LearningJeffery Dick, Saptarshi Nath, Christos Peridis … Andrea SoltoggioCoLLAs
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  10. 2024
    An Empirical Analysis of Forgetting in Pre-trained Models with Incremental Low-Rank UpdatesAlbin Soutif-Cormerais, Simone Magistri, J. Weijer, Andew D. BagdanovCoLLAs
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  14. 2024
    Integrating Present and Past in Unsupervised Continual LearningYipeng Zhang, Laurent Charlin, R. Zemel, Mengye RenCoLLAs
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  15. 2024
    Disentangling the Causes of Plasticity Loss in Neural NetworksClare Lyle, Zeyu Zheng, Khimya Khetarpal … Will DabneyCoLLAs
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  18. 2024
    Memory Head for Pre-Trained Backbones in Continual LearningMatteo Tiezzi, F. Becattini, Simone Marullo, S. MelacciCoLLAs
  19. 2024
    Continual Learning for Unsupervised Concept Bottleneck DiscoveryLuca Salvatore Lorello, Marco Lippi, S. MelacciCoLLAs
  20. 2024
    Replaying with Realistic Latent Vectors in Generative Continual LearningHyemin Jeong, Seong-Woong Kim, Dong-Wan ChoiCoLLAs
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.