  Generative Adversarial Networks (GANs) have been used extensively and quite
successfully for unsupervised learning. As GANs don't approximate an explicit
probability distribution, it's an interesting study to inspect the latent space
representations learned by GANs. The current work seeks to push the boundaries
of such inspection methods to further understand in more detail the manifold
being learned by GANs. Various interpolation and extrapolation techniques along
with vector arithmetic is used to understand the learned manifold. We show
through experiments that GANs indeed learn a data probability distribution
rather than memorize images/data. Further, we prove that GANs encode
semantically relevant information in the learned probability distribution. The
experiments have been performed on two publicly available datasets - Large
Scale Scene Understanding (LSUN) and CelebA.
