Related work

The foundational work on continual learning, 1989 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 5,456Sort Recent · Most cited
  1. 2026
    Elastic Multi-Gradient Descent for Parallel Continual LearningFan Lyu, Wei Feng, Yuepan Li … Liang WangTPAMI · Universitat Autònoma de Barcelona · Tianjin University · +2
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  2. 2026
    Dual Domain-Attribute Learning Framework With Asynchronous Adapters for Continual Test-Time AdaptationYuntong Tian, Kang Li, Tianyang He … Wei FengTIP · Tianjin University · University of Electronic Science and Technology of China · +1
  3. 2025
    FedAGC: Federated Continual Learning with Asymmetric Gradient CorrectionC T Zhang, Fanhua Shang, Hongyin Liu … Wei FengICCV · Tianjin University · Tianjin Medical University
  4. 2025
    Beyond Background Shift: Rethinking Instance Replay in Continual Semantic SegmentationHongmei Yin, Tingliang Feng, Fan Lyu … Liang WanCVPR · Tianjin University · Institute of Automation
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  5. 2023
    Measuring Asymmetric Gradient Discrepancy in Parallel Continual LearningFan Lyu, Qing Sun, Fanhua Shang … Wei FengICCV · Tianjin University
  6. 2023
    Multi-Label Continual Learning Using Augmented Graph Convolutional NetworkKaile Du, Fan Lyu, Linyan Li … Hanjing ChengIEEE Trans. Multimedia · Suzhou University of Science and Technology · Southeast University · +2
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  7. 2022
    AGCN: Augmented Graph Convolutional Network for Lifelong Multi-Label Image RecognitionKaile Du, Fan Lyu, Fuyuan Hu … Qiming FuIEEE International Conference on Multimedia and Expo (ICME) · Suzhou University of Science and Technology · Tianjin University · +1
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  8. 2021
    Multi-Domain Multi-Task Rehearsal for Lifelong LearningFan Lyu, Shuai Wang, Wei Feng … Song WangAAAI · Tianjin University · University of South Carolina · +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 lists only 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. 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.