Pseudo-labeling

Pseudo-Labeling is a semi-supervised learning technique in which unlabeled data are assigned predicted labels generated by a model trained on labeled data, thereby expanding the training set with high-confidence predictions to improve model performance.

499

Publications

32.6K

Citations

2K

Authors

497

Institutions

Publications per year

2017–2026

490

Authors

2K

  1. YS
    Yinghuan Shi

    Nanjing University, China

    7 publications · 667 citations

    7

    h-index

  2. YG
    Yixiao Ge

    Tencent (China), China

    6 publications · 737 citations

    6

    h-index

  3. TL
    Tatiana Likhomanenko

    Meta (Israel), Israel

    6 publications · 287 citations

    6

    h-index

  4. GS
    Gabriel Synnaeve

    Meta (Israel), Israel

    6 publications · 287 citations

    6

    h-index

  5. QX
    Qiantong Xu

    Meta (Israel), Israel

    6 publications · 314 citations

    6

    h-index

1–5 of 2K

Venues

  1. arXiv (Cornell University)

    Repository · 73 publications · 5.6K citations

    29

    h-index

  2. 24

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 24 publications · 1.8K citations

    21

    h-index

  4. IEEE Transactions on Medical Imaging

    15 publications · 875 citations

    15

    h-index

  5. IEEE Access

    16 publications · 553 citations

    14

    h-index

1–5