  We present two new metrics for evaluating generative models in the
class-conditional image generation setting. These metrics are obtained by
generalizing the two most popular unconditional metrics: the Inception Score
(IS) and the Fre'chet Inception Distance (FID). A theoretical analysis shows
the motivation behind each proposed metric and links the novel metrics to their
unconditional counterparts. The link takes the form of a product in the case of
IS or an upper bound in the FID case. We provide an extensive empirical
evaluation, comparing the metrics to their unconditional variants and to other
metrics, and utilize them to analyze existing generative models, thus providing
additional insights about their performance, from unlearned classes to mode
collapse.
