  One popular generative model that has high-quality results is the Generative
Adversarial Networks(GAN). This type of architecture consists of two separate
networks that play against each other. The generator creates an output from the
input noise that is given to it. The discriminator has the task of determining
if the input to it is real or fake. This takes place constantly eventually
leads to the generator modeling the target distribution. This paper includes a
study into the actual weights learned by the network and a study into the
similarity of the discriminator and generator networks. The paper also tries to
leverage the similarity between these networks and shows that indeed both the
networks may have a similar structure with experimental evidence with a novel
shared architecture.
