GANs are a battle between the discriminator and the generator. If the G places eyes in the wrong place, the D and G eventually get error signals that cause them

GANs are a battle between the discriminator and the generator. If the G places eyes in the wrong place, the D and G eventually get error signals that cause them to improve. Using pooling layers, CNNs build features that look across the entire image. So it can learn that eyes need to go across each other and above the nose. It is just that when used for classification, CNNs may not need to learn this because it is never trained to discriminate real and fake faces.