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The Fréchet Coefficient (FC) is a reliable and interpretable metric for evaluating generative models, including GANs and diffusion models. Unlike Fréchet Distance, including FID, i.e., its InceptionV3-based version, FC demonstrates stability across feature extractors, robustness to feature dimensionality, and a bounded scale [0–1], enabling transparent and consistent comparisons. Experiments on CIFAR-10, CelebA-HQ, AFHQv2, FFHQ, and ImageNet datasets show FC’s ability to track model quality improvements and distinguish subtle differences across architectures. Its computational efficiency and versatility make it a superior alternative to traditional metrics, positioning FC as a robust tool for the standardized evaluation of generative models across diverse datasets and feature extractors.

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The Fréchet Coefficient for GANs and Diffusion Models Evaluation

  • 会议论文

  • 发表日期：

    2026年

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  • Adrian Kucharski,
  • Anna Fabijańska

摘要

The Fréchet Coefficient (FC) is a reliable and interpretable metric for evaluating generative models, including GANs and diffusion models. Unlike Fréchet Distance, including FID, i.e., its InceptionV3-based version, FC demonstrates stability across feature extractors, robustness to feature dimensionality, and a bounded scale [0–1], enabling transparent and consistent comparisons. Experiments on CIFAR-10, CelebA-HQ, AFHQv2, FFHQ, and ImageNet datasets show FC’s ability to track model quality improvements and distinguish subtle differences across architectures. Its computational efficiency and versatility make it a superior alternative to traditional metrics, positioning FC as a robust tool for the standardized evaluation of generative models across diverse datasets and feature extractors.