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.

8 papers of 8,653Sort Recent · Most cited
  1. 2023
    Diving into Continual Ultra-fine-grained Visual CategorizationPengcheng Zhang, Xiaohan Yu, Xiao Bai … Yongsheng GaoInternational Conference on Digital Image Computing: Tech… · State Key Laboratory of Software Development Environment · Beihang University · +1
  2. 2023
    Decision Boundary Optimization for Few-shot Class-Incremental LearningChenxu Guo, Qi Zhao, Shuchang Lyu … Guangliang ChengICCV · Beihang University · University of Liverpool
  3. 2023
    SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained ModelGengwei Zhang, Liyuan Wang, Guoliang Kang … Yunchao WeiICCV · University of Technology Sydney · Tsinghua University · +2
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  4. 2023
    More task-balanced Class-Incremental LearningJunzhi Chen, Tian Wang, Deyuan Liu … Hichem SoussiYouth Academic Annual Conference of Chinese Association o… · Beihang University · Beijing Academy of Artificial Intelligence · +4
  5. 2023
    A Continual Learning Method for Reducing Class Interference Based on ReplayZhibo Xu, Tian Wang, Jian Wang … Hichem SnoussiChinese Control Conference (CCC) · Beihang University · Wuhan University · +6
  6. 2023
    AttriCLIP: A Non-Incremental Learner for Incremental Knowledge LearningRunqi Wang, Xiaoyue Duan, Guoliang Kang … Baochang ZhangCVPR · Huawei Technologies (Sweden) · Beihang University · +1
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  7. 2023PDF ↗
  8. 2023
    Unbiased and Efficient Self-Supervised Incremental Contrastive LearningCheng Ji, Jianxin Li, Hao Peng … Philip S. YuWSDM · Beihang University · Macquarie University · +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. 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.