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

• URL: http://arxiv.org/abs/2102.08921v1
• Date: Wed, 17 Feb 2021 18:25:30 GMT
• Title: How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
• Authors: Ahmed M. Alaa, Boris van Breugel, Evgeny Saveliev, Mihaela van der Schaar
• Abstract summary: We introduce a 3-dimensional evaluation metric 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.
• Score: 95.8037674226622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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