I just started reading about GAN theory properly for the first time and I have a question about a comment in the original GAN paper.
On page two there's a paragraph that states the following:
... had developed more general stochastic backpropagation rules, allowing one to backpropagate through Gaussian distributions with finite variance, and to backpropagate to the covariance parameter as well as the mean.
Can anyone explain what backpropagating through a distribution means? In other words, what's the concrete meaning of this?