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.

5 papers of 8,653Sort Recent · Most cited
  1. 2026
    Dynamic content-addressable memory based on global centroid features for online task-free continual learningCong Tu Tran, Thanh Tuan Nguyen, Thanh Tuan Nguyen, Nadège Thirion-MoreauMachine Vision and Applications · Centre National de la Recherche Scientifique · Université de Toulon · +4
  2. 2025
    FOCUS: Frequency-Optimized Conditioning of diffUSion models for mitigating catastrophic forgetting during test-time adaptationGabriel Tjio, Jie Zhang, Xulei Yang … Qing GuoMachine Vision and Applications · Agency for Science, Technology and Research · Nanyang Technological University · +4
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  3. 2024
    Future-proofing class-incremental learningQuentin Jodelet, Xin Liu, Yin Jun Phua, Tsuyoshi MurataMachine Vision and Applications · Tokyo Institute of Technology · National Institute of Advanced Industrial Science and Technology
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  4. 2023
    Online continual learning with saliency-guided experience replay using tiny episodic memoryGobinda Saha, Kaushik RoyMachine Vision and Applications · Purdue University West Lafayette
  5. 2022
    Utilizing incremental branches on a one-stage object detection framework to avoid catastrophic forgettingJeng-Lun Shieh, Muhamad Amirul Haq, Qazi Mazhar ul Haq … Peter ChondroMachine Vision and Applications · National Taiwan University of Science and Technology · Industrial Technology Research Institute
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.