Assessing Generative Models via Precision and Recall

Understand

Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison.

• Commonly used evaluation methods, such as the Frechet Inception Distance (FID), correlate well with the perceived quality of samples and are sensitive to mode dropping.
• However, these metrics are unable to distinguish between different failure cases since they only yield one-dimensional scores.
• We propose a novel definition of precision and recall for distributions which disentangles the divergence into two separate dimensions.