Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 11,817Sort Recent · Most cited
  1. 2022
    Efficient Bayesian Policy Reuse With a Scalable Observation Model in Deep Reinforcement LearningJinmei Liu, Zhi Wang, Chunlin Chen, Daoyi DongTNNLS · Nanjing University · University of Canberra · +1
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  2. 2022
    Towards Generalized Deepfake Detection With Continual Learning On Limited New Data: Anonymous AuthorsHe Huang, Nan Sun, Xufeng Lin, Nour MoustafaInternational Conference on Digital Image Computing: Tech… · University of Canberra · UNSW Sydney · +1
  3. 2022
    Learning Bayesian Sparse Networks with Full Experience Replay for Continual LearningQingsen Yan, Dong Gong, Yuhang Liu … Qinfeng ShiCVPR · Australian Centre for Robotic Vision · The University of Adelaide · +1
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  4. 2022
    Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight RegularizationFrancesco Pelosin, Saurav Jha, Andrea Torsello … Joost van de WeijerCVPR · Ca' Foscari University of Venice · UNSW Sydney · +2
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  5. 2022
    A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement LearningZhi Wang, Chunlin Chen, Daoyi DongIEEE Trans. Cybernetics · Nanjing University · University of Canberra · +1
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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.