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.

5 papers of 6,984Sort Recent · Most cited
  1. 2024
    TactCLNet: Tactile Continual Learning Network Based on Generative Replay for Object Hardness RecognitionYiwen Liu, Zhengkun Yi, Senlin Fang … Xinyu WuIEEE Transactions · Chinese Academy of Sciences · Shenzhen Institutes of Advanced Technology · +3
  2. 2023
    Topology-Aware Graph Convolution Network for Few-Shot Incremental 3-D Object LearningBingtao Ma, Yang Cong, Jiahua DongIEEE Transactions · Shenyang Institute of Automation · Chinese Academy of Sciences · +1
  3. 2021
    Hierarchical Lifelong Machine Learning With “Watchdog”Gan Sun, Yang Cong, Changjun Gu … Haibin YuIEEE Transactions · Shenyang Institute of Automation · Chinese Academy of Sciences · +2
  4. 2021
    MFS: A Brain-Inspired Memory Formation System for GANYifan Chang, Yifan Wang, Jian Peng … Wenbo LiIEEE Transactions · University of Science and Technology of China · Anhui University · +3
  5. 2018
    Guided Policy Search for Sequential Multitask LearningFangzhou Xiong, Biao Sun, Xu Yang … Zhiyong LiuIEEE Transactions · University of Chinese Academy of Sciences · University of Science and Technology Beijing · +4
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.