We present a novel metric for generative modeling evaluation, focusing primarily on generative networks. The method uses dendrograms to represent real and fake data, allowing for the divergence between training and generated samples to be computed. This metric focus on mode collapse, targeting generators that are not able to capture all modes in the training set. To evaluate the proposed method it is introduced a validation scheme based on sampling from real datasets, therefore the metric is evaluated in a controlled environment and proves to be competitive with other state-of-the-art approaches.

翻译：本文提出一种用于生成模型评估的新颖指标，主要关注生成网络。该方法利用树状图表示真实数据与生成数据，从而计算训练样本与生成样本之间的散度。该指标聚焦于模式坍塌问题，针对无法捕获训练集中全部模态的生成器进行评估。为验证所提方法，本文引入基于真实数据集采样的验证方案，在受控环境中验证该指标，并证明其与其它先进方法相比具有竞争力。