Der Fréchet Gründung Distance (FID) is a metric used to evaluate the quality of images generated by generativen Modellen, particularly in the context of Deep Learning and Computer Vision. It is commonly employed to assess the performance of Generative Adversarial Networks (GANs) and other Bildsynthese Techniken.
FID berechnet die Distanz zwischen zwei Wahrscheinlichkeitsverteilungen: one representing the generated images and the other representing real images from a dataset. To compute FID, the images are first passed through a pre-trained Inception v3 neural network, which extracts feature representations of the images. These features are then modeled as multivariate Gaussian distributions, characterized by their mean and covariance.
Der FID-Wert wird mit der Fréchet-Distanz berechnet, die quantifiziert, wie far apart these two distributions are. A lower FID score indicates that the generated images are more similar to the real images, suggesting better quality and diversity in the outputs of the generative model. Conversely, a higher FID score indicates poorer quality and greater divergence from real images.
Insgesamt dient FID als eine robuste Bewertungsmetrik, allowing researchers and practitioners to compare various generative models effectively. It has become a standard benchmark in the field of generative modeling, helping to advance the development of high-quality image synthesis techniques.