  Generative models are increasingly able to produce remarkably high quality
images and text. The community has developed numerous evaluation metrics for
comparing generative models. However, these metrics do not effectively quantify
data diversity. We develop a new diversity metric that can readily be applied
to data, both synthetic and natural, of any type. Our method employs random
network distillation, a technique introduced in reinforcement learning. We
validate and deploy this metric on both images and text. We further explore
diversity in few-shot image generation, a setting which was previously
difficult to evaluate.
