  We present a novel mathematical model that seeks to capture the key design
feature of generative adversarial networks (GANs). Our model consists of two
interacting spin glasses, and we conduct an extensive theoretical analysis of
the complexity of the model's critical points using techniques from Random
Matrix Theory. The result is insights into the loss surfaces of large GANs that
build upon prior insights for simpler networks, but also reveal new structure
unique to this setting.
