Semi-supervised Labeling

Semi-Supervised Labeling is a data annotation methodology within image labeling that combines a small set of manually labeled examples with a larger set of unlabeled data to improve labeling accuracy through iterative learning, thereby reducing the dependency on extensive human-annotated datasets.

160

Publications

7.7K

Citations

608

Authors

255

Institutions

Publications per year

2017–2026

118

Authors

608

  1. ML
    Mirella Lapata

    University of Edinburgh, United Kingdom

    4 publications · 168 citations

    4

    h-index

  2. QX
    Qizhe Xie

    Carnegie Mellon University, United States

    3 publications · 553 citations

    3

    h-index

  3. ZD
    Zihang Dai

    Google (United States), United States

    3 publications · 553 citations

    3

    h-index

  4. HF
    Hagen Fürstenau

    Saarland University, Germany

    3 publications · 142 citations

    3

    h-index

  5. QV
    Quoc V. Le

    Google (United States), United States

    3 publications · 553 citations

    3

    h-index

1–5 of 608

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 172 citations

    9

    h-index

  2. Neurocomputing

    4 publications · 72 citations

    4

    h-index

  3. IEEE Access

    4 publications · 95 citations

    4

    h-index

  4. IEEE Transactions on Medical Imaging

    3 publications · 101 citations

    3

    h-index

  5. Sensors

    3 publications · 106 citations

    3

    h-index

1–5