# Does a better discriminator in GANs mean better sample generation by the generator?

Since the discriminator defines how the generator is updated, then building a discriminator with a higher number of parameters/more layers should lead to a better quality of generated samples. So, assuming that it won't lead to overwhelming the generator (discriminator loss toward 0) or mode collapse, when engineering a GAN, I should build a discriminator as good as possible?

So, if you see here, both generator and discriminator and competing against each other and on the other side they are dependent on each other for efficient training.