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.

5 papers of 8,653Sort Recent · Most cited
  1. 2024
    Fast and Accurate Continual Test Time Domain AdaptationHaihang Wu, Bohan Zhuangon Continual Learning meets Multimodal Foundation Models:… · Monash University
  2. 2024
    FAM-Logo: Forward Compatible Multimodal Framework for Few-Shot Logo Incremental ClassificationSujuan Hou, Jianxin Zhan, Hao Xiongon Continual Learning meets Multimodal Foundation Models:… · Shandong Normal University · Macquarie University
  3. 2024
    Continual Learning meets Multimodal Foundation Models: Fundamentals and AdvancesWenbin Li, Qi Fan, Rui Yan … Jiebo Luoon Continual Learning meets Multimodal Foundation Models:… · Nanjing University · Systems Engineering Society of China · +3
  4. 2024
    EAGLE Network: A Novel Incremental Learning Framework for Detecting Unknown Logos in Open-World EnvironmentsZhongming Yuan, Hao Xiong, Sujuan Houon Continual Learning meets Multimodal Foundation Models:… · Shandong Normal University · Macquarie University
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  5. 2024
    Incremental Image Generation with Diffusion Models by Label Embedding Initialization and FusionBing Li, Dongdong Ren, Hao Liu … Yang Gaoon Continual Learning meets Multimodal Foundation Models:… · Nanjing University · Tencent (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. 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.