Generative Evaluation

Generative Evaluation is a methodological framework within the broader domain of generative modeling that specifically assesses the fidelity, diversity, and semantic alignment of generated data outputs against the underlying data distribution and intended application context.

67

Publications

4.8K

Citations

270

Authors

88

Institutions

Publications per year

2017–2026

51

Authors

270

  1. AN
    Austin Narcomey

    Stanford University, United States

    2 publications · 106 citations

    2

    h-index

  2. RK
    Ranjay Krishna

    Stanford University, United States

    2 publications · 106 citations

    2

    h-index

  3. AB
    Ali Borji

    University of Central Florida, United States

    2 publications · 60 citations

    2

    h-index

  4. BV
    Bart Vanneste

    University College London, United Kingdom

    2 publications · 160 citations

    2

    h-index

  5. MG
    Mitchell Gordon

    Stanford University, United States

    2 publications · 106 citations

    2

    h-index

1–5 of 270

Venues

  1. arXiv (Cornell University)

    Repository · 14 publications · 1.1K citations

    12

    h-index

  2. Computer Vision and Image Understanding

    CVIU · 3 publications · 90 citations

    3

    h-index

  3. SSRN Electronic Journal

    Repository · 2 publications · 44 citations

    2

    h-index

  4. Journal of Mechanical Design

    2 publications · 137 citations

    2

    h-index

  5. IEEE Access

    2 publications · 769 citations

    2

    h-index

1–5