Unsupervised Classification with Generative Models

It has been my impression, that in the immense space of Artificial Intelligence (AI) concepts and tools, Generative Adversarial Networks (GANs) stand aside as an untamed beast. Everybody realizes how powerful and cool they are, few know how to train them, and even fewer can actually find any use for them for a practical task. I might be wrong, so feel free to correct me. Meanwhile, I would like to take another look at this wonderful machinery and investigate its possible use for classification and embedding. GANs were introduced in reference [1]. They consist of two parts -- a discriminator and a generator. A discriminator is a function that takes in an object and converts it into a number. Of course, depending on the complexity of the object, it might be a formidable task to turn it into a number. For that reason, we might employ a pretty sophisticated function for a discriminator, like, for instance, a deep layered Convolutional Neural Network (CNN).