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
    From Knowledge Forgetting to Accumulation: Evolutionary Relation Path Passing for Lifelong Knowledge Graph EmbeddingJing Yang, Xinfa Jiang, Xiaowen Jiang … Shundong YangSIGIR · Zhengzhou University · Hainan University
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  2. 2024
    A class-incremental learning approach for learning feature-compatible embeddingsHongchao An, Jing Yang, Xiuhua Zhang … Jianjun HuNeural Networks · Guizhou University · Guizhou Minzu University · +3
  3. 2024
    CL-BPUWM: continuous learning with Bayesian parameter updating and weight memoryYao He, Jing Yang, Shaobo Li … Qing JiComplex & Intelligent Systems · Guizhou University · University of South Carolina · +1
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  4. 2023
    Uncertainty-Aware Few-Shot Class-Incremental LearningJiancai Zhu, Jiabao Zhao, Jiayi Zhou … Zhi ZhangICASSP · East China Normal University · New York University Shanghai · +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.