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. 2022
    Unveiling the Tapestry: The Interplay of Generalization and Forgetting in Continual LearningZenglin Shi, Jie Jing, Ying Sun … Mengmi ZhangTNNLS · Nanyang Technological University · Agency for Science, Technology and Research
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  2. 2021
    Tuned Compositional Feature Replays for Efficient Stream LearningMorgan B. Talbot, Rushikesh Zawar, Rohil Badkundri … Gabriel KreimanTNNLS · Boston Children's Hospital · Harvard–MIT Division of Health Sciences and Technology · +6
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  3. 2024
    Adaptive Visual Scene Understanding: Incremental Scene Graph GenerationNaitik Khandelwal, Xiao Liu, Mengmi ZhangNeurIPS
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  4. 2023
    Label-Efficient Online Continual Object Detection in Streaming VideoJay Zhangjie Wu, David Junhao Zhang, Wynne Hsu … Mike Zheng ShouICCV · National University of Singapore · Agency for Science, Technology and Research · +1
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  5. 2023
    Learning to Learn: How to Continuously Teach Humans and MachinesParantak Singh, You Li, Ankur Sikarwar … Mengmi ZhangICCV · Agency for Science, Technology and Research · Nanyang Technological University · +5
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  6. 2023PDF ↗
  7. 2023
    Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA TaskStan Weixian Lei, Difei Gao, Jay Zhangjie Wu … Mike Zheng ShouAAAI
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  8. 2019
    Variational Prototype Replays for Continual LearningMengmi Zhang, Tao Wang, Joo‐Hwee Lim … Jiashi FengarXiv · Harvard University Press
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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.