What is the difference between sampling diversity and sample fidelity?

Sampling diversity and sample fidelity are two key concepts in generative models, each addressing different aspects of output quality. Sampling diversity refers to how varied the generated samples are relative to the training data and each other. High diversity means the model produces outputs that cover a broad range of possibilities within the data distribution. Sample fidelity, on the other hand, measures how closely generated samples match the real data in terms of accuracy, detail, or realism. High fidelity implies outputs are indistinguishable from genuine data, even if they are less varied.

For example, consider a generative adversarial network (GAN) trained on images of animals. High sampling diversity would mean the model generates many species (e.g., dogs, cats, birds) with different poses, colors, and backgrounds. Low diversity might result in only producing variations of a single animal type. High fidelity would ensure each generated image is sharp, anatomically plausible, and free of artifacts. A low-fidelity model might create blurry or distorted animals, even if the outputs are diverse. Balancing these aspects depends on the application: a creative art tool might prioritize diversity, while a medical imaging model would prioritize fidelity.

These concepts often trade off. For instance, a variational autoencoder (VAE) might generate diverse samples by sampling from a broader latent space but lose fidelity if the model oversimplifies details. Conversely, a highly tuned GAN could produce photorealistic images (high fidelity) but fail to explore rare or novel data patterns (low diversity). Developers must adjust model architectures, training objectives (e.g., adding diversity penalties), or evaluation metrics (e.g., Fréchet Inception Distance for fidelity) to align with project goals. In code generation, diversity could mean suggesting multiple algorithms for a problem, while fidelity ensures the code is syntactically correct and efficient. Understanding this balance helps tailor models to specific use cases.