LAIT’s (Prof. Jaejun Yoo) paper accepted to NeurIPS 2023
TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity
in Generative Models
Pumjun Kim, Yoojin Jang, Jisu Kim, and Jaejun Yoo*
We propose a robust and reliable evaluation metric for generative models called Topological Precision and Recall (TopP&R, pronounced “topper”), which systematically estimates supports by retaining only topologically and statistically significant features with a certain level of confidence. TopP&R reliably evaluates the sample quality and ensures statistical consistency in its results. Our theoretical and experimental findings reveal that TopP&R provides a robust evaluation, accurately capturing the true trend of change in samples.