I am new to GANs. I noticed that everybody generates a random vector (usually 100 dimensional) from a standard normal distribution $N(0, 1)$. My question is: why? Why don't they sample these vectors from a uniform distribution $U(0, 1)$? Does the standard normal distribution has some properties that other probability distributions don't have?
It has become our human bias that data will arrive from a normal distribution. It is also the most prevalent distribution in nature occurring in many places. Hence, we sample from a normal distribution. Also, central limit theorem works around means lying around normal distribution.
It is not taboo to use others if they are helpful to your network. But, a data from one distribution can transformed into other and if that is something that is required, the network will to do it since it can approximate anything (although a weak assumption, but, hey, its working right?).