Automatic Label Generation

Automatic Label Generation is a methodological framework within labeling systems that autonomously produces descriptive labels for data instances by leveraging algorithmic rules or learned patterns, distinguishing itself from manual or semi-supervised approaches through its complete reliance on automated processes.

134

Publications

6.1K

Citations

549

Authors

211

Institutions

Publications per year

2017–2026

104

Authors

549

  1. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    3 publications · 69 citations

    3

    h-index

  2. SY
    Shicai Yang

    Teer Coatings (United Kingdom), United Kingdom

    2 publications · 148 citations

    2

    h-index

  3. PY
    Peng Yuan

    Houston Methodist, United States

    2 publications · 148 citations

    2

    h-index

  4. ZS
    Zhongnan Su

    Northeastern University, United States

    2 publications · 100 citations

    2

    h-index

  5. JZ
    Jiawei Zhu

    Nanjing Tech University, China

    2 publications · 83 citations

    2

    h-index

1–5 of 549

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 217 citations

    9

    h-index

  2. 3

    h-index

  3. NeuroImage

    2 publications · 95 citations

    2

    h-index

  4. Knowledge and Information Systems

    2 publications · 75 citations

    2

    h-index

  5. IEEE Robotics and Automation Letters

    2 publications · 168 citations

    2

    h-index

1–5