This paper shows that deep learning (DL) representations of data produced by generative adversarial nets (GANs) are random vectors which fall within the class of so-called concentrated random vectors. Further exploiting the fact that Gram matrices, of the type = with =[_1,...,_n]∈^p× n and _i independent concentrated random vectors from a mixture model, behave asymptotically (as n,p→∞) as if the _i were drawn from a Gaussian mixture, suggests that DL representations of GAN-data can be fully desc…