GAN - why doesn't the generator nullify the noise input? In GAN architecture, during training, what keeps the generator's output dependant on the input noise? Why don't the weights of the noise input become zero (plus a bias)?
I would expect the generator to converge to outputting a single picture which is extremely real and non-distinguishable from a real picture, and ignore the noise all together, since this is a "cheaper" way (in convergence time and in number of parameters used) to decrease the generator's loss.
If the generator always outputs the same image, then it's easy for the discriminator to win the game and tell apart the output of the generator from random images in the training set: if the input to the discriminator is that one image, then it outputs "came from the generator", otherwise outputs "came from the training step".  The game is set up so that the generator is rewarded for fooling the discriminator.  Always outputting the same image isn't going to fool the discriminator.