  Generative Adversarial Networks (GANs) are notoriously difficult to train
especially for complex distributions and with limited data. This has driven the
need for tools to audit trained networks in human intelligible format, for
example, to identify biases or ensure fairness. Existing GAN audit tools are
restricted to coarse-grained, model-data comparisons based on summary
statistics such as FID or recall. In this paper, we propose an alternative
approach that compares a newly developed GAN against a prior baseline. To this
end, we introduce Cross-GAN Auditing (xGA) that, given an established
"reference" GAN and a newly proposed "client" GAN, jointly identifies
intelligible attributes that are either common across both GANs, novel to the
client GAN, or missing from the client GAN. This provides both users and model
developers an intuitive assessment of similarity and differences between GANs.
We introduce novel metrics to evaluate attribute-based GAN auditing approaches
and use these metrics to demonstrate quantitatively that xGA outperforms
baseline approaches. We also include qualitative results that illustrate the
common, novel and missing attributes identified by xGA from GANs trained on a
variety of image datasets.
