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Communication Dans Un Congrès Année : 2019

Revisiting Precision and Recall Definition for Generative Model Evaluation

Résumé

In this article we revisit the definition of Precision-Recall (PR) curves for generative models proposed by Sajjadi et al. (arXiv:1806.00035). Rather than providing a scalar for generative quality, PR curves distinguish mode-collapse (poor recall) and bad quality (poor precision). We first generalize their formulation to arbitrary measures, hence removing any restriction to finite support. We also expose a bridge between PR curves and type I and type II error rates of likelihood ratio classifiers on the task of discriminating between samples of the two distributions. Building upon this new perspective, we propose a novel algorithm to approximate precision-recall curves, that shares some interesting methodological properties with the hypothesis testing technique from Lopez-Paz et al (arXiv:1610.06545). We demonstrate the interest of the proposed formulation over the original approach on controlled multi-modal datasets.

Dates et versions

hal-02131217 , version 1 (16-05-2019)

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Loïc Simon, Ryan Webster, Julien Rabin. Revisiting Precision and Recall Definition for Generative Model Evaluation. International Conference on Machine Learning (ICML), Jun 2019, Long Beach, United States. ⟨hal-02131217⟩
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