Inception Score (IS) and Frechet Inception Distance (FID) are metrics used to evaluate the quality and diversity of images generated by machine learning models, such as GANs. Both rely on pre-trained neural networks (often Inception v3) to assess how “realistic” generated images appear and how well they match the characteristics of real data. Here’s how they work and where they apply:
Inception Score (IS) measures two key properties: image quality (how recognizable objects are) and diversity (how varied the generated images are). It uses the Inception v3 model to classify generated images into predefined classes (e.g., “dog,” “car”). The score is calculated by comparing the probability distribution of predicted classes across all generated images. A high IS indicates that the model produces distinct, classifiable images (low entropy per image) and a wide variety of classes (high entropy across the dataset). For example, if a GAN generates only blurry cats, the IS would be low because the classifier can’t confidently assign classes, and diversity is poor. If it generates sharp, varied images of animals and vehicles, the IS increases.
Frechet Inception Distance (FID) compares the statistical similarity between generated images and real images. Instead of class probabilities, FID uses features extracted from an intermediate layer of the Inception network. It calculates the Frechet distance (a measure of distribution similarity) between the feature vectors of real and generated data. Lower FID values mean the generated images are closer to real ones in terms of visual features. For instance, if a model produces images with realistic textures and shapes but slightly distorted edges, FID would quantify how much those distortions deviate from real data. Unlike IS, FID directly compares generated and real data, making it less prone to overestimating quality when diversity is artificially inflated.
Application in Practice Developers use IS and FID to guide model training and compare architectures. For example, during GAN training, a rising IS suggests improving image clarity and diversity, while a dropping FID indicates the outputs are aligning better with real data. However, each metric has trade-offs: IS is fast to compute but ignores real data statistics, while FID is more robust but requires a large sample of real images. A practical workflow might involve using IS for quick iterations and FID for final validation. For instance, a model generating synthetic faces could achieve a high IS if the faces are diverse and recognizable but might still have a high FID if skin textures or lighting don’t match real portraits. By combining both metrics, developers gain a more complete picture of model performance.