mlsanityData to 14 Oct 2026

A New Perspective on Precision and Recall for Generative Models

arXiv 2511.02414 ↗First posted 4 Nov 2025cs.AIcited by 0

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Abstract (arXiv)

With the recent success of generative models in image and text, the question of their evaluation has recently gained a lot of attention. While most methods from the state of the art rely on scalar metrics, the introduction of Precision and Recall (PR) for generative model has opened up a new avenue of research. The associated PR curve allows for a richer analysis, but their estimation poses several challenges. In this paper, we present a new framework for estimating entire PR curves based on a binary classification standpoint. We conduct a thorough statistical analysis of the proposed estimates. As a byproduct, we obtain a minimax upper bound on the PR estimation risk. We also show that our framework extends several landmark PR metrics of the literature which by design are restrained to the extreme values of the curve. Finally, we study the different behaviors of the curves obtained experimentally in various settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 4 Nov 2025.

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References · 16

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  3. 2022Classifier-Free Diffusion Guidancecited by 5,847
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  5. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
  6. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  7. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 944
  8. 2021MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontierscited by 470
  9. 2020Reliable Fidelity and Diversity Metrics for Generative Modelscited by 434
  10. 2018Assessing Generative Models via Precision and Recallcited by 406
  11. 2022MAUVE Scores for Generative Models: Theory and Practicecited by 36
  12. 2023TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Modelscited by 14
4 more
  1. 2021Evaluating generative networks using Gaussian mixtures of image featurescited by 11
  2. 2023Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Modelscited by 11
  3. 2020On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximitycited by 6
  4. 2023Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensionscited by 5

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