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.

9 papers of 8,653Sort Recent · Most cited
  1. 2026
    Enhancing Federated Class-Incremental Learning via Spatial-Temporal Statistics AggregationZenghao Guan, Guojun Zhu, Zhou Yucan … Xiaoyan GuWWW · Institute of Information Engineering · University of Chinese Academy of Sciences · +3
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  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
    One-Shot Replay: Boosting Incremental Object Detection via Retrospecting One ObjectDongbao Yang, Yu Zhou, Xiaopeng Hong … Weiping WangAAAI · Institute of Information Engineering · University of Chinese Academy of Sciences · +2
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  4. 2022
    Lifelong Fine-Grained Image RetrievalWei Chen, Haoyang Xu, Nan Pu … Michael S. LewIEEE Trans. Multimedia · Xidian University · Leiden University · +2
  5. 2022
    Multi-View Correlation Distillation for Incremental Object DetectionDongbao Yang, Yu Zhou, Aoting Zhang … Qixiang YePattern Recognition · Chinese Academy of Sciences · Institute of Information Engineering · +1
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  6. 2022
    RD-IOD: Two-Level Residual-Distillation-Based Triple-Network for Incremental Object DetectionDongbao Yang, Yu Zhou, Wei Shi … Weiping WangACM Transactions · University of Chinese Academy of Sciences · Chinese Academy of Sciences · +1
  7. 2021
    Feature Estimations Based Correlation Distillation for Incremental Image RetrievalWei Chen, Yu Liu, Nan Pu … Michael S. LewIEEE Trans. Multimedia · Leiden University · Dalian University of Technology · +1
  8. 2020PDF ↗
  9. 2020
    On the Exploration of Incremental Learning for Fine-grained Image RetrievalWei Chen, Yu Liu, Weiping Wang … Michael S. LewBMVC · Leiden University · KU Leuven · +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.