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. 2025
    Continual Learning Beyond Experience Rehearsal and Full Model SurrogatesP. Bhat, Laurens Niesten, Elahe Arani, Bahram ZonoozarXiv
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  2. 2025
    Parameter Efficient Continual Learning with Dynamic Low-Rank AdaptationP. Bhat, Shakib Yazdani, Elahe Arani, Bahram ZonoozTrans. Mach. Learn. Res.
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  3. 2025
    Semantic Aware Representation Learning for Lifelong LearningFahad Sarfraz, Elahe Arani, Bahram ZonoozICLR
  4. 2024PDF ↗
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  7. 2024
    IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual LearningP. Bhat, Bharath Renjith, Elahe Arani, Bahram ZonoozTrans. Mach. Learn. Res.
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  8. 2023
    Continual Learning of Unsupervised Monocular Depth from VideosHemang Chawla, Arnav Varma, Elahe Arani, Bahram ZonoozWACV
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  9. 2023
    Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong LearningShruthi Gowda, Bahram Zonooz, Elahe AraniTrans. Mach. Learn. Res.
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  10. 2023PDF ↗
  11. 2023
    BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningKishaan Jeeveswaran, Prashant Bhat, Bahram Zonooz, E. AraniICML
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  12. 2023PDF ↗
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  15. 2023
    Dynamically Modular and Sparse General Continual LearningArnav Varma, E. Arani, Bahram ZonoozVISIGRAPP
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  16. 2022
    Sparse Coding in a Dual Memory System for Lifelong LearningFahad Sarfraz, Elahe Arani, Bahram ZonoozAAAI
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  17. 2022PDF ↗
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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.