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. 2026
    Dynamic LoRA-Experts and Prototype-Ensemble Matching for Class-Incremental LearningHongwei Zhao, Rui Liu, Ying LiuApplied Sciences · Beihang University
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  2. 2025
    D-Know: Disentangled Domain Knowledge-Aided Learning for Open-Domain Continual Object DetectionBintao He, Caixia Yan, Yan Kou … Yugui XieApplied Sciences · Xi'an Jiaotong University · Digital Video (Italy)
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  3. 2023
    Class-wise Classifier Design Capable of Continual Learning using Adaptive Resonance Theory-based Topological ClusteringNaoki Masuyama, Yusuke Nojima, Farhan Dawood, Zongying LiuApplied Sciences · Osaka Metropolitan University · Tokyo Metropolitan University · +3
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  4. 2023
    Deep Lifelong Learning Optimization Algorithm in Dense Region FusionLinghao Zhang, Fan Ding, Siyu Xiang … Hongjun WangApplied Sciences · Inner Mongolia Electric Power (China) · Southwest Jiaotong University
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  5. 2022
    A Federated Incremental Learning Algorithm Based on Dual Attention MechanismKai Hu, Meixia Lu, Yaogen Li … Yi YangApplied Sciences · Nanjing University of Information Science and Technology · Nanjing University of Science and Technology
  6. 2021
    Bayesian Optimization Based Efficient Layer Sharing for Incremental LearningBomi Kim, Taehyeon Kim, Yoonsik ChoeApplied Sciences · Yonsei University
  7. 2020
    A Novel Layer Sharing-based Incremental Learning via Bayesian OptimizationYoonsik Choe, Bomi Kim, Taehyeon KimApplied Sciences · Yonsei University
  8. 2018
    Transfer Incremental Learning using Data AugmentationGhouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia … Michel JézéquelApplied Sciences · Université de Bretagne Occidentale · Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance · +2
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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.