Jaccard Loss

Jaccard Loss is a loss function in machine learning that penalizes the dissimilarity between predicted and true sets by measuring the complement of the Jaccard index, defined as the ratio of the intersection to the union of the sets.

55

Publications

4.3K

Citations

218

Authors

97

Institutions

Publications per year

2017–2026

52

Variants

Intersection Over Union Loss

Authors

218

  1. MB
    Matthew B. Blaschko

    KU Leuven, Belgium

    5 publications · 1.5K citations

    5

    h-index

  2. MB
    Maxim Berman

    KU Leuven, Belgium

    5 publications · 1.5K citations

    5

    h-index

  3. AR
    Amal Rannen Triki

    KU Leuven, Belgium

    3 publications · 1K citations

    3

    h-index

  4. XX
    Xiao‐Liang Xie

    Chinese Academy of Sciences, China

    2 publications · 79 citations

    2

    h-index

  5. XZ
    Xiao-Hu Zhou

    Chinese Academy of Sciences, China

    2 publications · 79 citations

    2

    h-index

1–5 of 218

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 367 citations

    9

    h-index

  2. IEEE Access

    4 publications · 132 citations

    4

    h-index

  3. IEEE Transactions on Medical Imaging

    2 publications · 1.1K citations

    2

    h-index

  4. 2

    h-index

  5. Medical Image Analysis

    1 publications · 21 citations

    1

    h-index

1–5