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.

13 papers of 8,653Sort Recent · Most cited
  1. 2026
    DELNet: Continuous All-in-One Weather Removal via Dynamic Expert LibraryShihong Liu, K. Zuo, Hanguang XiaoImage and Vision Computing
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  2. 2025
    Invariant prompting with classifier rectification for continual learningChunsing Lo, Hao Zhang, J. AndyImage and Vision Computing · Sun Yat-sen University · Key Laboratory of Guangdong Province
  3. 2025
    Memory augmented using diffusion model for class-incremental learningQuentin Jodelet, Xin Liu, Yin Jun Phua, Tsuyoshi MurataImage and Vision Computing · Tokyo Institute of Technology · National Institute of Advanced Industrial Science and Technology
  4. 2025
    Towards on-device continual learning with Binary Neural Networks in industrial scenariosLorenzo Vorabbi, Angelo Carraggi, D. Maltoni … Stefano SantiImage and Vision Computing
  5. 2025
    Exemplar-free class incremental action recognition based on self-supervised learningChunyu Hou, Yonghong Hou, Jinyin Jiang, Gunel AbdullayevaImage and Vision Computing · Tianjin University
  6. 2024
    Background debiased class incremental learning for video action recognitionLe Quan Nguyen, Jinwoo Choi, L. Minh Dang, Hyeonjoon MoonImage and Vision Computing · Sejong University · Kyung Hee University
  7. 2024
    Utilizing Inherent Bias for Memory Efficient Continual Learning: A Simple and Robust BaselineNeela Rahimi, Ming ShaoImage and Vision Computing · University of Massachusetts Dartmouth
  8. 2024
    Few-shot class incremental learning via prompt transfer and knowledge distillationFeidu Akmel, Fanman Meng, Mingyu Liu … Elias LemuyeImage and Vision Computing · University of Electronic Science and Technology of China
  9. 2021
    Generative feature-driven image replay for continual learningKevin Thandiackal, Tiziano Portenier, Andrea Giovannini … Orçun GökselImage and Vision Computing · ETH Zurich · IBM Research - Zurich · +1
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  10. 2023
    RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic SegmentationChang Liu, Giulia Rizzoli, Francesco Barbato … P. ZanuttighImage and Vision Computing
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  11. 2021
    Incremental human action recognition with dual memoryMatheus Gutoski, André Eugênio Lazzaretti, Heitor Silvério LopesImage and Vision Computing · Universidade Tecnológica Federal do Paraná
  12. 2021
    Task-based parameter isolation for foreground segmentation without catastrophic forgetting using multi-scale region and edges fusion networkIslam Osman, Agwad ElTantawy, Mohamed ShehataImage and Vision Computing · University of British Columbia · University of British Columbia, Okanagan Campus · +1
  13. 2020
    Cuepervision: self-supervised learning for continuous domain adaptation without catastrophic forgettingMark Schutera, Frank M. Hafner, Jochen Abhau … Markus ReischlImage and Vision Computing · Karlsruhe Institute of Technology · ZF Friedrichshafen (Germany)
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. By default it shows the 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. The rest are one click away under “All papers”. 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.