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.

4 papers of 8,653Sort Recent · Most cited
  1. 2025
    GCD: Advancing Vision-Language Models for Incremental Object Detection via Global Alignment and Correspondence DistillationXu Wang, Zilei Wang, Zihan LinAAAI · University of Science and Technology of China
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  2. 2024
    Category-instance distillation based on visual-language models for rehearsal-free class incremental learningWeilong Jin, Zilei Wang, Yixin ZhangIET Computer Vision · University of Science and Technology of China
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  3. 2019
    Learning a Unified Classifier Incrementally via RebalancingSaihui Hou, Xinyu Pan, Chen Change Loy … Dahua LinCVPR · University of Science and Technology of China · XLAB (Slovenia) · +3
  4. 2018
    Lifelong Learning via Progressive Distillation and RetrospectionSaihui Hou, Xinyu Pan, Chen Change Loy … Dahua LinECCV · University of Science and Technology of China · Chinese University of Hong Kong · +1
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.