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.

6 papers of 8,653Sort Recent · Most cited
  1. 2023
    C2MR: Continual Cross-Modal Retrieval for Streaming Multi-modal DataHuaiwen Zhang, Yang Yang, Fan Qi … Changsheng XuACM International Conference on Multimedia · Inner Mongolia University · Tianjin University of Technology · +2
    PDF ↗
  2. 2023
    Dynamic Equilibrium-Based Continual Learning Model with Disentangled Meta-featuresMingyi Zhang, Junge ZhangIEEE International Conference on Systems, Man, and Cybern… · Chinese Academy of Sciences · Shandong Institute of Automation
  3. 2023
    A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational costTielin Zhang, Xiang Cheng, Shuncheng Jia … Bo XuScience Advances · Chinese Academy of Sciences · Shandong Institute of Automation · +3
    PDF ↗
  4. 2023
    Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural NetworksBing Han, Feifei Zhao, Yi Zeng … Guobin ShenIJCAI · Chinese Academy of Sciences · Shandong Institute of Automation · +2
    PDF ↗
  5. 2023
    Mixture Uniform Distribution Modeling and Asymmetric Mix Distillation for Class Incremental LearningSunyuan Qiang, Jiayi Hou, Jun Wan … Du ZhangAAAI · Macau University of Science and Technology · Lafayette College · +2
    PDF ↗
  6. 2023
    Imitating the oracle: Towards calibrated model for class incremental learningFei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng‐Lin LiuNeural Networks · Chinese Academy of Sciences · Shandong Institute of Automation · +3
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.