I'm just starting to learn about GAN's (Generative Adversarial Networks), and I have a question about how they work. I can't be the the first person to wonder about this, I just haven't seen this addressed anywhere.
Suppose a GAN is trained in a typical fashion, so that it can produce images which are, to humans, imperceptibly different from images that a separate, discriminator network can correctly classify, and yet these new images will be misclassified by the discriminator network with high probability.
My question is: is the ability of the GAN to fool the discriminator specific to the particular discriminator? Suppose two discriminators were trained on slightly different data sets, or the same data set, but with different initializations for the discriminator's weights. Then would a GAN trained against one discriminator preform reasonably well on the second? Any references would be very useful.