Inception Score (IS) and Fréchet Inception Distance (FID) are two metrics commonly used to evaluate the quality of images generated by machine learning models, especially Generative Adversarial Networks (GANs). Both scores help to assess how well a model can produce images that not only look realistic but also represent a diverse range of classes or categories. The Inception Score measures the diversity and quality of the generated images, while the FID provides a comparison between the statistics of the generated images and those of real images, offering a more comprehensive understanding of the model's performance.
The Inception Score works by using a pre-trained Inception model, which is a type of convolutional neural network. When you generate images using a GAN or any generative model, you feed the images into the Inception model to obtain class predictions. The Inception Score focuses on two aspects: the clarity of the images and their diversity. High Inception Scores indicate that the model generates images that are easily classified into specific categories (high clarity) and that the generated images cover a broad range of these categories (high diversity). For instance, if a GAN is trained to generate images of animals, a high Inception Score would suggest that the model is creating clear and varied images of different animals like dogs, cats, and birds.
On the other hand, the Fréchet Inception Distance takes a different approach. It calculates the distance between the feature representations of real and generated images, also using the Inception model. Both sets of images are passed through the model to extract features, which are then represented as multivariate Gaussian distributions. FID quantifies the difference between these two distributions: lower FID values indicate a closer match between real and generated images. For example, if a GAN is designed to produce landscapes and achieves a low FID, it means that the generated landscapes are not only well-formed but also closely resemble the real landscapes in terms of color, texture, and composition. By using both IS and FID, developers can obtain a comprehensive assessment of their models, ensuring that they produce high-quality and visually appealing images.