The evaluation of GANs is important because it helps us understand what the characteristics of the model we trained are and what we can achieve with it. In this chapter, we will be asking these questions:
- Do the fake samples have an image quality that is similar to the real samples?
- Do the fake samples have a variety that is similar to the real samples?
- Do the fake samples satisfy the specifications of the real samples?
Notice that by asking these questions, we can evaluate our model and specify what we can achieve with it. For example, a model with a low variety in samples, but good image quality, can be used, whereas a model with relatively bad image quality but a good variety produces noisy data that can be used to regularize another model and help it to generalize lower quality images.
Despite its relative youth, several publications (Arjovsky and...
