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.

7 papers of 6,984Sort Recent · Most cited
  1. 2026
    DesCLIP: Robust Continual Learning via General Attribute Descriptions for VLM-Based Visual RecognitionChiyuan He, Zihuan Qiu, Fanman Meng … Hui LiIEEE Trans. Multimedia · University of Electronic Science and Technology of China
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  2. 2025
    Leveraging Pre-Trained Models for Multimodal Class-Incremental Learning under Adaptive FusionYukun Chen, Zihuan Qiu, Fanman Meng … Qingbo WuICASSP · University of Electronic Science and Technology of China
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  3. 2025
    MINGLE: Mixture of Null-Space Gated Low-Rank Experts for Test-Time Continual Model MergingZihuan Qiu, Yidong Xu, Chiyuan He … Hongliang LiNeurIPS · University of Electronic Science and Technology of China · Amazon (Germany) · +1
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  4. 2025
    Distribution-Level Memory Recall for Continual Learning: Preserving Knowledge and Avoiding ConfusionShaoxu Cheng, Kanglei Geng, Chiyuan He … Hongliang LiIEEE Trans. Multimedia · University of Electronic Science and Technology of China
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  5. 2024
    Dual-Consistency Model Inversion for Non-Exemplar Class Incremental LearningZihuan Qiu, Yi Xu, Fanman Meng … Qingbo WuCVPR · University of Electronic Science and Technology of China · Dalian University of Technology
  6. 2023
    GFR: Generic feature representations for class incremental learningZhichuan Wang, Linfeng Xu, Zihuan Qiu … Hongliang LiNeurocomputing · University of Electronic Science and Technology of China
  7. 2022
    ISM-Net: Mining incremental semantics for class incremental learningZihuan Qiu, Linfeng Xu, Zhichuan Wang … Hongliang LiNeurocomputing · University of Electronic Science and Technology of China
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.