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The ability to automatically estimate the quality and coverage of the samplesproduced by a generative model is a vital requirement for driving algorithmresearch. We present an evaluation metric that can separately and reliablymeasure both of these aspects in image generation tasks by forming explicit,non-parametric representations of the manifolds of real and generated data. Wedemonstrate the effectiveness of our metric in StyleGAN and BigGAN by providingseveral illustrative examples where existing metrics yield uninformative orcontradictory results. Furthermore, we analyze multiple design variants ofStyleGAN to better understand the relationships between the model architecture,training methods, and the properties of the resulting sample distribution. Inthe process, we identify new variants that improve the state-of-the-art. Wealso perform the first principled analysis of truncation methods and identifyan improved method. Finally, we extend our metric to estimate the perceptualquality of individual samples, and use this to study latent spaceinterpolations.

Improved Precision and Recall Metric for Assessing Generative Models