Synthetic Data for Computer Vision Workshop at CVPR 2026 · 2026
Fréchet Inception Distance is Failing to Preserve Rank Consistency for Synthetic Out-of-Distribution Samples
Why this publication matters
Researchers often use a single score to decide which image generator is better. This publication examines whether a widely used score keeps that ordering trustworthy when the images come from unfamiliar distributions. The question matters because an unreliable ranking can direct development toward the wrong model.
Abstract
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Figures
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Cite this paper
@inproceedings{liu2026frechetinceptiondistance85,
title = {{Fréchet Inception Distance is Failing to Preserve Rank Consistency for Synthetic Out-of-Distribution Samples}},
author = {Linghui Liu and Henrike Stephani and Janis Keuper},
booktitle = {Synthetic Data for Computer Vision Workshop at CVPR 2026},
year = {2026},
url = {https://openreview.net/pdf?id=jWqcKaZdTS}
}
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