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. 2026
    C3GAN: A brain-inspired memory consolidation for class-incremental learningLin Xiong, Tao Wang, Fuqing Zhang … Hailing XiongNeural Networks · Southwest University · Chongqing Jiaotong University · +2
  2. 2023
    Slow-Fast Time Parameter Aggregation Network for Class-Incremental Lip ReadingXueyi Zhang, Chengwei Zhang, Tao Wang … Haizhou LiACM International Conference on Multimedia · National University of Defense Technology · Shenzhen Research Institute of Big Data · +3
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  3. 2023
    Few-Shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge DistillationChengjia Jiang, Tao Wang, Sien Li … Antonios AntoniouInternational Conference on Information Science, Parallel… · Minjiang University · Wuyi University · +2
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  4. 2019
    Variational Prototype Replays for Continual LearningMengmi Zhang, Tao Wang, Joo‐Hwee Lim … Jiashi FengarXiv · Harvard University Press
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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.