Quantifying Manifolds: Do the manifolds learned by Generative
  Adversarial Networks converge to the real data manifold
Anupam Chaudhuri Anj Simmons Mohamed Abdelrazek
This paper presents our experiments to quantify the manifolds learned by ML
models (in our experiment, we use a GAN model) as they train. We compare the
manifolds learned at each epoch to the real manifolds representing the real
data. To quantify a manifold, we study the intrinsic dimensions and topological
features of the manifold learned by the ML model, how these metrics change as
we continue to train the model, and whether these metrics convergence over the
course of training to the metrics of the real data manifold.