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 6,984Sort Recent · Most cited
  1. 2023
    Semantic-visual Guided Transformer for Few-shot Class-incremental LearningWenhao Qiu, Sichao Fu, Jingyi Zhang … Qinmu PengIEEE International Conference on Multimedia and Expo (ICME) · Huazhong University of Science and Technology
    PDF ↗
  2. 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 ↗
  3. 2023
    Rethinking Self-Supervision for Few-Shot Class-Incremental LearningLinglan Zhao, Jing Lü, Zhanzhan Cheng … Xiangzhong FangIEEE International Conference on Multimedia and Expo (ICME) · Shanghai Jiao Tong University · University of Science and Technology of China · +1
  4. 2023
    Decoupled Mutual Distillation for Incremental Object DetectionGao-Dong Liu, Wan‐Lei Zhao, Jie ZhaoIEEE International Conference on Multimedia and Expo (ICME) · Xiamen University · University of Nottingham Ningbo China
  5. 2023
    Discriminative Gradient Adjustment with Coupled Knowledge Distillation for Class Incremental LearningHao Zhang, Yanxu Hu, Jiawen Peng, J. AndyIEEE International Conference on Multimedia and Expo (ICME) · Sun Yat-sen University · Ministry of Education of the People's Republic of China · +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. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led 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.