Diffusion models, which are popular in generative tasks such as image synthesis, rely on specific evaluation metrics to assess their performance. Common metrics include Inception Score (IS), Fréchet Inception Distance (FID), and Structural Similarity Index Measure (SSIM). Each of these metrics offers a different perspective on how well the generated outputs compare to real data.
The Inception Score (IS) evaluates how well the generated images can be classified by a pre-trained Inception model. For IS, higher scores indicate that generated images are more distinct and diverse. However, IS has limitations, as it only considers the quality of images and not how closely they resemble the training set. On the other hand, Fréchet Inception Distance (FID) is often favored because it compares the distribution of generated images to real images. FID calculates the distance between the feature representations of real and generated images extracted from a specific layer of the Inception model. A lower FID score signifies better performance, as it indicates that the generated images are closer in distribution to the real ones.
Another important metric is the Structural Similarity Index Measure (SSIM), which assesses the similarity between two images based on luminance, contrast, and structural information. SSIM focuses more on perceptual quality and can be useful for evaluating image generation tasks where maintaining visual fidelity is crucial. While FID and IS are often used for overall assessment, developers can incorporate SSIM when they want to ensure the generated content looks more like the reference images. Each of these metrics plays a role in understanding the strengths and weaknesses of diffusion models, enabling developers to make informed decisions when it comes to optimizing their models for specific applications.