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.

이전 글 RVI Lab’s (Prof. Kyungdon Joo) papers accepted to ICCV 2021!

다음 글 3rd place in HANDS challenge: Motion reconstruction held in ICCV’23