A Study into the similarity in generator and discriminator in GAN architecture.

One popular generative model that has high-quality results is the GenerativeAdversarial Networks(GAN). This type of architecture consists of two separatenetworks that play against each other. The generator creates an output from theinput noise that is given to it. The discriminator has the task of determiningif the input to it is real or fake. This takes place constantly eventuallyleads to the generator modeling the target distribution. This paper includes astudy into the actual weights learned by the network and a study into thesimilarity of the discriminator and generator networks. The paper also tries toleverage the similarity between these networks and shows that indeed both thenetworks may have a similar structure with experimental evidence with a novelshared architecture.