The objective function used in GANs is a quantitative measure that provides information about each player's performance, discriminator, and generator in the GAN game. For example, in the first GAN objective function, the output of the discriminator on fake data provides information about how well the generator is fooling the discriminator and how well the discriminator can identify real data. Although this information is useful because it provides information about the status of the minimax game and how close it is to equilibrium, it provides absolutely no information about the images themselves.
In this context, researchers in the GAN community have been developing quantitative methods that can be used to measure image quality, variety, and satisfaction of specifications.
In this section, we will address a few of such measures, including the following...