  Despite the success of Generative Adversarial Networks (GANs), mode collapse
remains a serious issue during GAN training. To date, little work has focused
on understanding and quantifying which modes have been dropped by a model. In
this work, we visualize mode collapse at both the distribution level and the
instance level. First, we deploy a semantic segmentation network to compare the
distribution of segmented objects in the generated images with the target
distribution in the training set. Differences in statistics reveal object
classes that are omitted by a GAN. Second, given the identified omitted object
classes, we visualize the GAN's omissions directly. In particular, we compare
specific differences between individual photos and their approximate inversions
by a GAN. To this end, we relax the problem of inversion and solve the
tractable problem of inverting a GAN layer instead of the entire generator.
Finally, we use this framework to analyze several recent GANs trained on
multiple datasets and identify their typical failure cases.
