Dendrogram distance: an evaluation metric for generative networks using
  hierarchical clustering
Gustavo Sutter Carvalho Moacir Antonelli Ponti
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.