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Fréchet Inception Distance (FID) is a metric used to evaluate the quality and diversity of generated images in the field of machine learning and generative modeling, particularly in the context of generative adversarial networks (GANs). It was proposed by Martin Heusel and his colleagues in the paper titled "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium" in 2017. The FID coefficient measures the similarity between the distribution of real images and the distribution of generated images. It is based on the idea of computing the Fréchet distance (a measure of similarity between two statistical distributions) between the feature representations of the real and generated images. Here's how the FID coefficient is calculated:
A smaller FID coefficient implies that the generated images are more similar to the real images in terms of their feature representations, which typically correlates with better image quality and diversity. It is important to note that while FID is a useful metric for comparing different GAN models or iterations during training, it might not always correlate perfectly with human judgment of image quality. FID is just one of several metrics used to evaluate GANs and other generative models. Researchers often use a combination of metrics to obtain a more comprehensive understanding of the model's performance.
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