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.

5 papers of 8,653Sort Recent · Most cited
  1. 2026
    HiCPS: Hierarchical complementary prompt synergy for few-shot class-incremental learningQiang Huang, Shengli Wu, Shaohua Wan … Hu LuNeurocomputing · Jiangsu University · University of Ulster · +4
  2. 2025
    Exploring Multimodal Prompts For Unsupervised Continuous Anomaly DetectionMingle Zhou, Jiahui Liu, Jin Wan … Min LiACM International Conference on Multimedia · Qilu University of Technology · Shandong Academy of Sciences
    PDF ↗
  3. 2025
    Plug-In Open-Set Cross-Modal HashingBowen Wang, Lei Zhu, Fengling Li … Jingjing LiIEEE Trans. Multimedia · Tongji University · University of Technology Sydney · +3
  4. 2021
    Prototype-Guided Memory Replay for Continual LearningStella Ho, Ming Liu, Lan Du … Yong XiangTNNLS · Deakin University · Monash University · +1
    PDF ↗
  5. 2022
    Semi-supervised Continual Learning with Meta Self-trainingStella Ho, Ming Liu, Lan Du … Shang GaoACM International Conference on Information & Knowled… · Deakin University · Monash University · +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.