  Devising domain- and model-agnostic evaluation metrics for generative models
is an important and as yet unresolved problem. Most existing metrics, which
were tailored solely to the image synthesis setup, exhibit a limited capacity
for diagnosing the different modes of failure of generative models across
broader application domains. In this paper, we introduce a 3-dimensional
evaluation metric, ($\alpha$-Precision, $\beta$-Recall, Authenticity), that
characterizes the fidelity, diversity and generalization performance of any
generative model in a domain-agnostic fashion. Our metric unifies statistical
divergence measures with precision-recall analysis, enabling sample- and
distribution-level diagnoses of model fidelity and diversity. We introduce
generalization as an additional, independent dimension (to the
fidelity-diversity trade-off) that quantifies the extent to which a model
copies training data -- a crucial performance indicator when modeling sensitive
data with requirements on privacy. The three metric components correspond to
(interpretable) probabilistic quantities, and are estimated via sample-level
binary classification. The sample-level nature of our metric inspires a novel
use case which we call model auditing, wherein we judge the quality of
individual samples generated by a (black-box) model, discarding low-quality
samples and hence improving the overall model performance in a post-hoc manner.
