Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

20 papers of 7,070Sort Recent · Most cited
  1. 2025
    Continual Learning Beyond Experience Rehearsal and Full Model SurrogatesPrashant Bhat, Laurens Niesten, Elahe Arani, Bahram ZonoozarXiv
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  2. 2026
    Parameter Efficient Continual Learning with Dynamic Low-Rank AdaptationPrashant Bhat, Shakib Yazdani, Elahe Arani, Bahram ZonoozTMLR
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  3. 2025
    Semantic Aware Representation Learning for Lifelong LearningFahad Sarfraz, Elahe Arani, Bahram ZonoozICLR
  4. 2024PDF ↗
  5. 2024
    Mitigating Interference in the Knowledge Continuum through Attention-Guided Incremental LearningPrashant Bhat, Bharath Renjith, Elahe Arani, Bahram ZonoozCoLLAs
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  6. 2024PDF ↗
  7. 2024
    IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual LearningPrashant Bhat, Bharath Renjith, Elahe Arani, Bahram ZonoozTMLR
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  8. 2024
    Continual Learning of Unsupervised Monocular Depth from VideosHemang Chawla, Arnav Varma, Elahe Arani, Bahram ZonoozWACV · Eindhoven University of Technology · TomTom (Netherlands)
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  9. 2023
    TriRE: A Multi-Mechanism Learning Paradigm for Continual Knowledge Retention and PromotionPreetha Vijayan, Prashant Bhat, Elahe Arani, Bahram ZonoozNeurIPS
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  10. 2023PDF ↗
  11. 2023
    BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningKishaan Jeeveswaran, Prashant Bhat, Bahram Zonooz, Elahe AraniICML
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  12. 2023PDF ↗
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  15. 2023
    Dynamically Modular and Sparse General Continual LearningArnav Varma, Elahe Arani, Bahram ZonoozVISIGRAPP
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  16. 2023
    Sparse Coding in a Dual Memory System for Lifelong LearningFahad Sarfraz, Elahe Arani, Bahram ZonoozAAAI
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  17. 2022PDF ↗
  18. 2022PDF ↗
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  20. 2022PDF ↗
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.