One of the biggest challenges in evaluating GANs samples is to understand how much of the real distribution the generator has learned. For example, let's consider the size of the support for the set of all the possible images of dogs. Naturally, this set must include millions of dog images that portray combinations of all dog features, including size, breed, hair color, pose, and more.
Assuming there are millions of dogs in real life that we humans perceive as unique, a GAN that has truly learned the distribution of dogs must be able to produce a similar number of unique dog images. Estimating the number of unique images of dogs a GAN is able to produce might seem like a daunting task at first, but researchers ...
