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.

我们提出了一种用于生成建模评估的新颖度量方法，主要关注生成网络。该方法使用树状图来表示真实数据和虚假数据，从而能够计算训练样本和生成样本之间的差异。该指标专注于模式坍塌，针对无法捕捉训练集中所有模式的生成器。为了评估所提出的方法，在真实数据集上进行了基于采样的验证方案引入，因此该指标在一个受控环境中进行评估，并且证明在与其他最先进的方法相比中具有竞争力。