Previous chapters introduced general-purpose metrics for assessing synthetic data quality, focusing on statistical fidelity and machine learning utility applicable across many scenarios. However, synthetic data generation often targets specific data modalities (like images or time-series) or employs distinct generative model architectures (such as GANs or VAEs), each presenting unique evaluation challenges. General metrics may not capture the specific properties or failure modes relevant to these specialized contexts.

This chapter addresses the need for tailored evaluation techniques. You will learn about metrics designed specifically for:

• Image Data: Understanding and applying measures like Fréchet Inception Distance (FID) and Inception Score (IS) to assess the quality and diversity of generated images.
• Text Data: Using metrics common in natural language processing, such as perplexity and BLEU scores, to evaluate synthetic text.
• Time-Series Data: Methods to evaluate the preservation of temporal dependencies, autocorrelation, and other dynamic properties.
• Specific Generative Models: Examining metrics often associated with GANs (e.g., related to discriminator performance) and VAEs (e.g., reconstruction loss, properties of the latent space).

By the end of this chapter, you will be equipped to select and apply appropriate specialized metrics to gain deeper insights into the quality of synthetic data generated for particular applications or by specific model types.