Many recent generative models leverage alternative generative frameworks, and among them, Generative Adversarial Networks (GANs) are one of the most popular. The basic idea behind a general GAN-based generative model is to first define a trainable generator network $g_{\theta}: R^{d}\rightarrow \chi$. This generator network is trained to generate realistic data samples $\tilde{x} \in \chi$ by taking a random seed $z \in R^{d}$ as input. At the same time, a discriminator network d_{phi}: chi rightarrow [0,1] is defined. The discriminator's goal is to distinguish between real data samples $x \in \chi$ and samples generated by the generator $\tilde{x} \in \chi$.