In the Gan paper it is said page 3 Figure 1:
"The lower horizontal line is the domain from which z is sampled, in this case uniformly. The horizontal line above is part of the domain of x. The upward arrows show how the mapping x = G(z) imposes the non-uniform distribution pg on transformed samples"
For those who wants to see the figure:
I wanted to know what that would mean in practical case. Let's say you are working with images that are normalized between [0,..,1] this would be the domain of x as referred in the paper right? Does this mean that I would have to sample my z from the domain of x, i.e: [0,..,1] ?
In most implementations I see people taking point randomly using things such as: np.random.randn(latent_dim)