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Devising domain- and model-agnostic evaluation metrics for generative modelsis an important and as yet unresolved problem. Most existing metrics, whichwere tailored solely to the image synthesis setup, exhibit a limited capacityfor diagnosing the different modes of failure of generative models acrossbroader application domains. In this paper, we introduce a 3-dimensionalevaluation metric, ($\alpha$-Precision, $\beta$-Recall, Authenticity), thatcharacterizes the fidelity, diversity and generalization performance of anygenerative model in a domain-agnostic fashion. Our metric unifies statisticaldivergence measures with precision-recall analysis, enabling sample- anddistribution-level diagnoses of model fidelity and diversity. We introducegeneralization as an additional, independent dimension (to thefidelity-diversity trade-off) that quantifies the extent to which a modelcopies training data -- a crucial performance indicator when modeling sensitivedata with requirements on privacy. The three metric components correspond to(interpretable) probabilistic quantities, and are estimated via sample-levelbinary classification. The sample-level nature of our metric inspires a noveluse case which we call model auditing, wherein we judge the quality ofindividual samples generated by a (black-box) model, discarding low-qualitysamples and hence improving the overall model performance in a post-hoc manner.

How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models