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 7,070Sort Recent · Most cited
  1. 2026
    CLIP-Based Class Incremental Semantic Segmentation Framework With Generalization-Preserving Knowledge DistillationQining Ren, Zhenyu Zhang, Depeng Li, Zhigang ZengIEEE TCSVT · Huazhong University of Science and Technology
  2. 2025
    Multiple semantic prompt for rehearsal-free continual learningJunwei Chen, Zhenyu Zhang, Depeng Li, Zhigang ZengNeural Networks · Huazhong University of Science and Technology
  3. 2025PDF ↗
  4. 2025
    HFT: Half Fine-Tuning for Large Language ModelsTingfeng Hui, Zhenyu Zhang, Shuohuan Wang … Hua WuACL
    PDF ↗
  5. 2020
    SEM: Adaptive Staged Experience Access Mechanism for Reinforcement LearningJianshu Wang, Xinzhi Wang, Xiangfeng Luo … Yang LiIEEE 32nd International Conference on Tools with Artifici… · Shanghai University of Engineering Science
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 written 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.