Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 6,984Sort Recent · Most cited
  1. 2024
    ESDB: Expand the Shrinking Decision Boundary via One-to-Many Information Matching for Continual Learning With Small MemoryKunchi Li, Hongyang Chen, Jun Wan, Shan YuIEEE TCSVT · Chinese Academy of Sciences · Shandong Institute of Automation · +3
  2. 2024
    From Data to Optimization: Data-Free Deep Incremental Hashing With Data Disambiguation and Adaptive ProxiesQinghang Su, Dayan Wu, Chenming Wu … Weiping WangIEEE TCSVT · Chinese Academy of Sciences · Institute of Information Engineering · +2
  3. 2023
    Continual Learning for Image Segmentation With Dynamic QueryWeijia Wu, Yuzhong Zhao, Zhuang Li … Mike Zheng ShouIEEE TCSVT · Zhejiang University · University of Chinese Academy of Sciences · +2
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  4. 2023
    IOSL: Incremental Open Set LearningBingtao Ma, Yang Cong, Yu RenIEEE TCSVT · Shenyang Institute of Automation · Chinese Academy of Sciences · +2
  5. 2023
    Self-Paced Weight Consolidation for Continual LearningWei Wei Cong, Yang Cong, Gan Sun … Jiahua DongIEEE TCSVT · Shenyang Institute of Automation · Chinese Academy of Sciences · +2
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  6. 2023
    Contrastive Correlation Preserving Replay for Online Continual LearningDan-Ping Yu, Mingyi Zhang, Mantian Li … Kaiqi HuangIEEE TCSVT · Harbin Institute of Technology · Chinese Academy of Sciences · +4
  7. 2022
    Lifelong Visual-Tactile Spectral Clustering for Robotic Object PerceptionYuyang Liu, Yang Cong, Gan Sun, Zhengming DingIEEE TCSVT · Shenyang Institute of Automation · Chinese Academy of Sciences · +2
  8. 2020
    Fast Adapting Without Forgetting for Face RecognitionHao Liu, Xiangyu Zhu, Zhen Lei … Stan Z. LiIEEE TCSVT · Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · +3
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.