# Unifying and extending Precision Recall metrics for assessing generative models

arXiv: https://arxiv.org/abs/2405.01611 · HTML: https://mlsanity.com/paper/2405.01611/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 9 of 9)

- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Improved Precision and Recall Metric for Assessing Generative Models](https://mlsanity.com/paper/1904.06991/) (2019)
- [Reliable Fidelity and Diversity Metrics for Generative Models](https://mlsanity.com/paper/2002.09797/) (2020)
- [Assessing Generative Models via Precision and Recall](https://mlsanity.com/paper/1806.00035/) (2018)
- [Universal consistency and rates of convergence of multiclass prototype algorithms in metric spaces](https://mlsanity.com/paper/2010.00636/) (2020)
- [Evaluating generative networks using Gaussian mixtures of image features](https://mlsanity.com/paper/2110.05240/) (2021)
- [Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models](https://mlsanity.com/paper/2309.01590/) (2023)
- [On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximity](https://mlsanity.com/paper/2006.11809/) (2020)
- [Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions](https://mlsanity.com/paper/2306.09618/) (2023)

## Built on by (most cited first, 1 of 1)

- [Position: All Current Generative Fidelity and Diversity Metrics are Flawed](https://mlsanity.com/paper/2505.22450/) (2025)

## Abstract (arXiv)

With the recent success of generative models in image and text, the evaluation of generative models has gained a lot of attention. Whereas most generative models are compared in terms of scalar values such as Frechet Inception Distance (FID) or Inception Score (IS), in the last years (Sajjadi et al., 2018) proposed a definition of precision-recall curve to characterize the closeness of two distributions. Since then, various approaches to precision and recall have seen the light (Kynkaanniemi et al., 2019; Naeem et al., 2020; Park & Kim, 2023). They center their attention on the extreme values of precision and recall, but apart from this fact, their ties are elusive. In this paper, we unify most of these approaches under the same umbrella, relying on the work of (Simon et al., 2019). Doing so, we were able not only to recover entire curves, but also to expose the sources of the accounted pitfalls of the concerned metrics. We also provide consistency results that go well beyond the ones presented in the corresponding literature. Last, we study the different behaviors of the curves obtained experimentally.