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.

17 papers of 8,653Sort Recent · Most cited
  1. 2023
    Teacher Agent: A Knowledge Distillation-Free Framework for Rehearsal-Based Video Incremental LearningShengqin Jiang, Yaoyu Fang, Haokui Zhang … Peng WangIJCV · Nanjing University of Information Science and Technology · Northwestern Polytechnical University · +3
    PDF ↗
  2. 2026
    R2A2-MoE: Ridge Regression-Based Analytic Adaptation with Mixture of Experts for Continual Learning with Vision-Language ModelsM. X. Liu, Quan Fang, Junyu Gao, Yang YangSpringer LNCS · Beijing University of Posts and Telecommunications · Chinese Academy of Sciences · +2
  3. 2025
    Feature Drift Oriented Distribution Reconstruction for Imbalanced Class Incremental LearningTingmin Li, Fengqiang Wan, Yipeng Lin, Yang YangFrontiers · Nanjing University of Science and Technology
  4. 2025
    Continual learning with Bayesian compression for shared and private latent representationsYang Yang, Dandan Guo, Bo Chen, Dexiu HuNeural Networks · Jilin University · Xidian University · +1
  5. 2025
  6. 2024PDF ↗
  7. 2024PDF ↗
  8. 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 ↗
  9. 2023
    Continual learning with Bayesian model based on a fixed pre-trained feature extractorYang Yang, Zhiying Cui, Junjie Xu … Ruixuan WangVisual Intelligence · Sun Yat-sen University · Peng Cheng Laboratory · +1
    PDF ↗
  10. 2023
    Lifelong learning with Shared and Private Latent Representations learned through synaptic intelligenceYang Yang, Jie Huang, Dexiu HuNeural Networks · PLA Information Engineering University
  11. 2021
    Cost-Effective Incremental Deep Model: Matching Model Capacity With the Least SamplingYang Yang, Da-Wei Zhou, De-Chuan Zhan … Jian YangTKDE · Nanjing University of Science and Technology · Southeast University · +2
  12. 2022
    Learning to Classify With Incremental New ClassDa-Wei Zhou, Yang Yang, De-Chuan ZhanTNNLS · Nanjing University · Nanjing University of Science and Technology
  13. 2021
    S2OSC: A Holistic Semi-Supervised Approach for Open Set ClassificationYang Yang, Hongchen Wei, Zhenqiang Sun … Jian YangACM Transactions · Nanjing University of Science and Technology · Nanjing Normal University · +3
    PDF ↗
  14. 2021
    Learning Adaptive Embedding Considering Incremental ClassYang Yang, Zhen-Qiang Sun, Hengshu Zhu … Jian YangTKDE · Nanjing University of Science and Technology · Nanjing Normal University · +4
    PDF ↗
  15. 2020
    Learning from the Past: Continual Meta-Learning with Bayesian Graph Neural NetworksYadan Luo, Zi Huang, Zheng Zhang … Yang YangAAAI · The University of Queensland · Harbin Institute of Technology · +1
    PDF ↗
  16. 2019
    Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and SustainabilityYang Yang, Da-Wei Zhou, De‐Chuan Zhan … Yuan JiangKDD · Nanjing University · Rutgers, The State University of New Jersey
  17. 2019
    Memorized Variational Continual Learning for Dirichlet Process MixturesYang Yang, Bo Chen, Hongwei LiuIEEE Access · Xidian University
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.