In the last few years, the great potentiality of deep learning has been proven by cutting-edge artificial intelligence applications. Ranging from well-defined tasks like object recognition or speech translation to the more generic autonomous driving, deep learning has been proven to be an extremely powerful tool. One fundamental concept on which deep learning is implicitly built upon is representation learning: how we represent data matters in how we understand the world. In this work, we first study the concept of representation learning, the methods to perform it and its various applications. With the goal of contextualizing this framework to deep generative models, we study the most popular generative methods and make a qualitative comparison between them.