论文标题
有条件图像生成的评估指标
Evaluation Metrics for Conditional Image Generation
论文作者
论文摘要
我们提出了两个新的指标，用于评估班级条件图像生成设置中的生成模型。这些指标是通过概括两个最受欢迎的无条件指标来获得的：INPECTION评分（IS）和Fre'Chet Inception距离（FID）。理论分析显示了每个提出的指标背后的动机，并将新颖指标与它们的无条件同行联系起来。在FID情况下，该链接以IS或上限为上限。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，从而提供有关其性能的更多见解，从未学习的类别到模式崩溃。
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.
