Active Learning For Verification

Active Learning for Verification is a machine learning approach that iteratively selects the most informative samples for manual verification to improve classifier performance while minimizing labeling effort in the context of reducing false positives.

7

Publications

1.5K

Citations

28

Authors

15

Institutions

Publications per year

2017–2026

3

Authors

28

  1. VG
    Vladimir G. Kim

    Adobe Systems (United States), United States

    1 publications · 1.3K citations

    1

    h-index

  2. JL
    Jens Lehmann

    University of Bonn, Germany

    1 publications · 59 citations

    1

    h-index

  3. HH
    Haisong Huang

    Guizhou University, China

    1 publications · 27 citations

    1

    h-index

  4. LG
    Leonidas Guibas

    Stanford University, United States

    1 publications · 1.3K citations

    1

    h-index

  5. HS
    Hao Su

    Worcester Polytechnic Institute, United States

    1 publications · 1.3K citations

    1

    h-index

1–5 of 28

Venues

  1. Symmetry

    1 publications · 27 citations

    1

    h-index

  2. ACM Transactions on Graphics

    TOG · 1 publications · 1.3K citations

    1

    h-index

  3. 1

    h-index

1–4