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This paper revisits the Precision-Recall (PR) curves for evaluating generative models, addressing limitations of previous definitions by generalizing them to arbitrary measures and linking them to type I and type II error rates in classification. A novel algorithm is proposed for approximating PR curves that improves upon existing methods by utilizing a classifier to separate samples from two distributions. The experimental results demonstrate the advantages of this new formulation in assessing generative m

Author

unruffledbassi8

Language

EN