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We propose a robust and reliable evaluation metric for generative models byintroducing topological and statistical treatments for rigorous supportestimation. Existing metrics, such as Inception Score (IS), Frechet InceptionDistance (FID), and the variants of Precision and Recall (P&R), heavily rely onsupports that are estimated from sample features. However, the reliability oftheir estimation has not been seriously discussed (and overlooked) even thoughthe quality of the evaluation entirely depends on it. In this paper, we proposeTopological Precision and Recall (TopP&R, pronounced 'topper'), which providesa systematic approach to estimating supports, retaining only topologically andstatistically important features with a certain level of confidence. This notonly makes TopP&R strong for noisy features, but also provides statisticalconsistency. Our theoretical and experimental results show that TopP&R isrobust to outliers and non-independent and identically distributed (Non-IID)perturbations, while accurately capturing the true trend of change in samples.To the best of our knowledge, this is the first evaluation metric focused onthe robust estimation of the support and provides its statistical consistencyunder noise.

TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models