  The recent advent of powerful generative models has triggered the renewed
development of quantitative measures to assess the proximity of two probability
distributions. As the scalar Frechet inception distance remains popular,
several methods have explored computing entire curves, which reveal the
trade-off between the fidelity and variability of the first distribution with
respect to the second one. Several of such variants have been proposed
independently and while intuitively similar, their relationship has not yet
been made explicit. In an effort to make the emerging picture of generative
evaluation more clear, we propose a unification of four curves known
respectively as: the precision-recall (PR) curve, the Lorenz curve, the
receiver operating characteristic (ROC) curve and a special case of R\'enyi
divergence frontiers. In addition, we discuss possible links between PR /
Lorenz curves with the derivation of domain adaptation bounds.
