  Devising indicative evaluation metrics for the image generation task remains
an open problem. The most widely used metric for measuring the similarity
between real and generated images has been the Fr\'echet Inception Distance
(FID) score. Because it does not differentiate the fidelity and diversity
aspects of the generated images, recent papers have introduced variants of
precision and recall metrics to diagnose those properties separately. In this
paper, we show that even the latest version of the precision and recall metrics
are not reliable yet. For example, they fail to detect the match between two
identical distributions, they are not robust against outliers, and the
evaluation hyperparameters are selected arbitrarily. We propose density and
coverage metrics that solve the above issues. We analytically and
experimentally show that density and coverage provide more interpretable and
reliable signals for practitioners than the existing metrics. Code:
https://github.com/clovaai/generative-evaluation-prdc.
