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 Fréchet 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.

我们提出了两个新的指标，用于在类条件图像生成设置中评估生成模型。这些度量是通过概括两个最流行的无条件度量获得的：初始得分（IS）和弗雷谢特初始距离（FID）。理论分析显示了每个提出的指标背后的动机，并将新颖的指标与它们的无条件对应联系起来。在IS的情况下，链接采用乘积的形式；在FID的情况下，链接采用上限的形式。我们提供了广泛的经验评估，将指标与它们的无条件变体和其他指标进行了比较，并利用它们来分析现有的生成模型，从而提供了关于其性能的更多见解，从未经学习的课程到模式崩溃。