La Fréchet Inicio Distance (FID) is a metric used to evaluate the quality of images generated by modelos generativos, particularly in the context of aprendizaje profundo and visión por computadora. It is commonly employed to assess the performance of Generative Adversarial Networks (GANs) and other síntesis de imágenes técnicas.
FID calcula la distancia entre dos distribuciones de probabilidad: 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.
La puntuación FID se calcula usando la distancia de Fréchet, que cuantifica qué tan 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.
En general, FID sirve como una métrica robusta métrica de evaluación, 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.