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.

24 papers of 8,653Sort Recent · Most cited
  1. 2026
    Bi-Compatible Task-Agnostic Feature Augmentation for Expansion-Based Class-Incremental LearningBowen Zheng, Zijun Shen, Da-Wei Zhou … De‐Chuan ZhanIJCV · Nanjing University
  2. 2026
    SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction TuningZhen-Hao Xie, Jun-Tao Tang, Yu-Cheng Shi … Da-Wei ZhouICML
    PDF ↗
  3. 2025PDF ↗
  4. 2025
    External Knowledge Injection for CLIP-Based Class-Incremental LearningDawei Zhou, Kaiwen Li, Jingyi Ning … De‐Chuan ZhanICCV · Nanjing University
    PDF ↗
  5. 2025PDF ↗
  6. 2025
    Task-Agnostic Guided Feature Expansion for Class-Incremental LearningBowen Zheng, Da-Wei Zhou, Han-Jia Ye, De‐Chuan ZhanCVPR · Nanjing University
    PDF ↗
  7. 2025
    Dual Consolidation for Pre-Trained Model-Based Domain-Incremental LearningDawei Zhou, Zi-Wen Cai, Han-Jia Ye … De‐Chuan ZhanCVPR · Artificial Intelligence in Medicine (Canada)
    PDF ↗
  8. 2025
    PILOT: a pre-trained model-based continual learning toolboxHailong Sun, Dawei Zhou, De‐Chuan Zhan, Han-Jia YeInformation Sciences · Nanjing University
    PDF ↗
  9. 2025
    Learning Without Forgetting for Vision-Language ModelsDa-Wei Zhou, Yuanhan Zhang, Yan Wang … Ziwei LiuTPAMI · Nanjing University · Nanyang Technological University
    PDF ↗
  10. 2025
    Adaptive adapter routing for long-tailed class-incremental learningZhihong Qi, Da-Wei Zhou, Yiran Yao … De-Chuan ZhanMachine Learning · Nanjing Xiaozhuang University
    PDF ↗
  11. 2025
    Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You NeedDa-Wei Zhou, Zi-Wen Cai, Han-Jia Ye … Ziwei LiuIJCV · Nanjing University · Nanyang Technological University
    PDF ↗
  12. 2024
    Class-Incremental Learning: A SurveyDa-Wei Zhou, Qiwei Wang, Zhihong Qi … Ziwei LiuTPAMI · Nanyang Technological University · Nanjing University
    PDF ↗
  13. 2024
    Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental LearningDa-Wei Zhou, Hailong Sun, Han-Jia Ye, De-Chuan ZhanCVPR · Nanjing University
    PDF ↗
  14. 2024
    Continual Learning with Pre-Trained Models: A SurveyDawei Zhou, Hailong Sun, Jingyi Ning … De‐Chuan ZhanIJCAI
    PDF ↗
  15. 2024
    Multi-layer Rehearsal Feature Augmentation for Class-Incremental LearningBowen Zheng, Da-Wei Zhou, Han-Jia Ye, De-Chuan ZhanICML
  16. 2023
    Few-Shot Class-Incremental Learning via Training-Free Prototype CalibrationQiwei Wang, Da-Wei Zhou, Yikai Zhang … Han-Jia YeNeurIPS
    PDF ↗
  17. 2023
    Preserving Locality in Vision Transformers for Class Incremental LearningBowen Zheng, Da-Wei Zhou, Han-Jia Ye, De‐Chuan ZhanIEEE International Conference on Multimedia and Expo (ICME) · Nanjing University
    PDF ↗
  18. 2023
    PyCIL: a Python toolbox for class-incremental learningDa-Wei Zhou, Fuyun Wang, Han-Jia Ye, De‐Chuan ZhanInformation Sciences · Nanjing University
    PDF ↗
  19. 2023
  20. 2023
    Few-Shot Class-Incremental Learning by Sampling Multi-Phase TasksDa-Wei Zhou, Han-Jia Ye, Liang Ma … De-Chuan ZhanTPAMI · Nanjing University
    PDF ↗
  21. 2022
    Forward Compatible Few-Shot Class-Incremental LearningDa-Wei Zhou, Fuyun Wang, Han-Jia Ye … De‐Chuan ZhanCVPR · Nanjing University
    PDF ↗
  22. 2023
    A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental LearningDa-Wei Zhou, Qiwei Wang, Han-Jia Ye, De‐Chuan ZhanICLR
    PDF ↗
  23. 2022
    FOSTER: Feature Boosting and Compression for Class-Incremental LearningFuyun Wang, Da-Wei Zhou, Han-Jia Ye, De‐Chuan ZhanECCV
    PDF ↗
  24. 2021
    Co-Transport for Class-Incremental LearningDa-Wei Zhou, Han-Jia Ye, De‐Chuan ZhanACM International Conference on Multimedia · Nanjing University
    PDF ↗
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.