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.

5 papers of 6,984Sort Recent · Most cited
  1. 2020
    MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental LearningHanbin Zhao, Yongjian Fu, Mintong Kang … Xi LiTPAMI · Zhejiang University of Science and Technology · Huawei Technologies (China)
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  2. 2020
    GraphSAIL: Graph Structure Aware Incremental Learning for Recommender SystemsYishi Xu, Yingxue Zhang, Wei Guo … Mark CoatesCIKM · Université de Montréal · Huawei Technologies (Canada) · +2
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  3. 2020
    Semantic Drift Compensation for Class-Incremental LearningLu Yu, Bartłomiej Twardowski, Xialei Liu … Joost van de WeijerCVPR · Universitat Autònoma de Barcelona · Northwestern Polytechnical University · +1
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  4. 2020
    Generative Feature Replay For Class-Incremental LearningXialei Liu, Chenshen Wu, Mikel Menta … Joost van de WeijerCVPR · Universitat Autònoma de Barcelona · University of Florence · +1
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  5. 2020
    More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental LearningYu Liu, Sarah Parisot, Greg Slabaugh … Tinne TuytelaarsECCV · KU Leuven · Huawei Technologies (China) · +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 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.