English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

In this article we revisit the definition of Precision-Recall (PR) curves forgenerative models proposed by Sajjadi et al. (arXiv:1806.00035). Rather thanproviding a scalar for generative quality, PR curves distinguish mode-collapse(poor recall) and bad quality (poor precision). We first generalize theirformulation to arbitrary measures, hence removing any restriction to finitesupport. We also expose a bridge between PR curves and type I and type II errorrates of likelihood ratio classifiers on the task of discriminating betweensamples of the two distributions. Building upon this new perspective, wepropose a novel algorithm to approximate precision-recall curves, that sharessome interesting methodological properties with the hypothesis testingtechnique from Lopez-Paz et al (arXiv:1610.06545). We demonstrate the interestof the proposed formulation over the original approach on controlledmulti-modal datasets.

Revisiting Precision and Recall Definition for Generative Model Evaluation