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

17 Feb 2021arXiv:2102.08921archive 2025-07-28

Ahmed M. Alaa, Boris van Breugel, Evgeny Saveliev, Mihaela van der Schaar

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, (α-Precision, β-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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amazon-science/tabsyn mentioned on GitHubpytorch report
fangzy96/tabcutmix mentioned on GitHubpytorch report
marcojira/fls mentioned on GitHubpytorch report

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3ran · our draft was wrong

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compute_alpha_precision vanderschaarlab/evaluating-generative-models/metrics/evaluation.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 9c5d287ad510cbe1 · report
geglu fangzy96/tabcutmix/tabsyn/model.py community (archive-listed) ran · our draft was wrong fingerprinted no licence file found · pointer only · 49f8fe0655f00ee5 · report
reglu fangzy96/tabcutmix/tabsyn/model.py community (archive-listed) ran · our draft was wrong fingerprinted no licence file found · pointer only · 58bd3831c7fb6729 · report

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Binary ClassificationDiversityImage Generation

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