Common
Fréchet inception distance
Metric used to assess image quality
Fréchet inception distance
The Fréchet inception distance (FID) is a metric used to assess the quality of images created by a generative model, like a generative adversarial network (GAN) or a diffusion model. The FID compares the distribution of generated images with the distribution of a set of real images (a "ground truth" set). Rather than comparing individual images, mean and covariance statistics of many images generated by the model are compared with the same statistics generated from images in the ground truth or reference set. A convolutional neural network such as an inception architecture is used to produce higher-level features describing the images, thus leading to the name Fréchet inception distance. The FID is inspired by the earlier inception score (IS) metric which evaluates only the distribution of generated images. The FID metric does not replace the IS metric; classifiers that achieve the best (lowest) FID score tend to have greater sample variety while classifiers achieving the best (highest...
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