Generative Adversarial Networks are a type of unsupervised machine learning algorithm proposed by Goodfellow et. al in 2014 (short and good article).
The simplest formulation of the model is based on a game theoretic approach, zero sum game, where we pit two neural networks against one another. We define two rival networks, one generator \( g \), and one discriminator \( d \). The generator directly produces samples $$ \begin{equation} x = g(z; \theta^{(g)}) \tag{1} \end{equation} $$