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.

6 papers of 11,817Sort Recent · Most cited
  1. 2022
    Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental LearningKai Zhu, Wei Zhai, Yang Cao … Zheng-Jun ZhaCVPR · University of Science and Technology of China · University of Rochester
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  2. 2021
    Self-Promoted Prototype Refinement for Few-Shot Class-Incremental LearningKai Zhu, Yang Cao, Wei Zhai … Zheng-Jun ZhaCVPR · University of Science and Technology of China · Huawei Technologies (United Kingdom)
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  3. 2021
    ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Image SegmentationXinyue Huo, Lingxi Xie, Jianzhong He … Qi TianCVPR · University of Science and Technology of China · Chinese Academy of Sciences
  4. 2021
    Image De-raining via Continual LearningMan Zhou, Jie Xiao, Yifan Chang … Zheng-Jun ZhaCVPR · University of Science and Technology of China · Nanjing University of Science and Technology
  5. 2019
    Learning a Unified Classifier Incrementally via RebalancingSaihui Hou, Xinyu Pan, Chen Change Loy … Dahua LinCVPR · University of Science and Technology of China · XLAB (Slovenia) · +3
  6. 2017
    StyleBank: An Explicit Representation for Neural Image Style TransferDongdong Chen, Lu Yuan, Jing Liao … Gang HuaCVPR · University of Science and Technology of China · Microsoft Research Asia (China)
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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.