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.

7 papers of 8,653Sort Recent · Most cited
  1. 2024
    Dynamic prompt allocation and tuning for continual test-time adaptationChaoran Cui, Yongrui Zhen, Shuai Gong … Yilong YinInformation Sciences · Shandong University of Finance and Economics · Shandong University
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  2. 2024
    HINT: Hypernetwork approach to training weight interval regions in continual learningPatryk Krukowski, Anna Bielawska, Kamil Książek … Przemysław SpurekInformation Sciences · Jagiellonian University · Narodowy Instytut Leków · +1
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  3. 2024
    Hybrid rotation self-supervision and feature space normalization for class incremental learningWenyi Feng, Zhe Wang, Qian Zhang … Zhilin FuInformation Sciences · Qinghai University · East China University of Science and Technology
  4. 2024
  5. 2024
    Cross-Domain Continual Learning via CLAMPWeiwei Weng, Mahardhika Pratama, Jie Zhang … Savitha RamasamyInformation Sciences · Nanyang Technological University · University of South Australia · +1
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  6. 2024
    Few-Shot Class Incremental Learning via Robust Transformer ApproachNaeem Paeedeh, Naeem Paeedeh, Mahardhika Pratama … Ryszard KowalczykInformation Sciences · University of South Australia · Universitas Gadjah Mada · +2
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  7. 2024
    Bridging pre-trained models to continual learning: A hypernetwork based framework with parameter-efficient fine-tuning techniquesFengqian Ding, Chen Xu, Han Liu … Hongchao ZhouInformation Sciences · Shandong University
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.