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.

6 papers of 8,653Sort Recent · Most cited
  1. 2025
    D-Know: Disentangled Domain Knowledge-Aided Learning for Open-Domain Continual Object DetectionBintao He, Caixia Yan, Yan Kou … Yugui XieApplied Sciences · Xi'an Jiaotong University · Digital Video (Italy)
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  2. 2025
    Continual Learning Model with Dynamic Adaptation and Flexibility for Incremental ClassesMinami Hotta, Nobuyuki Ogasawara, Kengo Miyajima … Masayuki GotoApplied Sciences · Waseda University
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  3. 2025
    Tail-Calibrated Transformer Autoencoding with Prototype- Guided Mining for Open-World Object DetectionMuhammad Ali Iqbal, Yeo Chan Yoon, Soo Kyun KimApplied Sciences · Jeju National University
  4. 2025
    Mitigating the Stability–Plasticity Trade-Off in Neural Networks via Shared Extractors in Class-Incremental LearningMingda Dong, Rui Li, Feng LiuApplied Sciences · East China Normal University · China Mobile (China) · +1
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  5. 2025
    Analysis of Model Merging Methods for Continual Updating of Foundation Models in Distributed Data SettingsKenta Kubota, Ren Togo, Keisuke Maeda … Miki HaseyamaApplied Sciences · Hokkaido University
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  6. 2025
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.