Discriminative models vs Generative model

One of the major division of modern machine learning is categorisation between discriminative vs generative modelling. A Discriminative models refers to class of models which learn to classify based on the probability estimates i.e p(y/X) where y is class label and X is the data point. Where as a Generative model explicitly models the distribution of each class by learning the joint probability p(X,y) between the inputs and class labels and then use bayes rule to classify p(y/X). There are plethora of reasons why one might find a generative process very fascinating. One of the many reasons is that by using a generative model we can understand the causal relationships between variations in the data and the output observations and based on that form an explainable hypothesis. Another important feature of using generative modelling is being able to find disentangled factors in the data which constitute to various data generating factors. Two of the most majorly use data generative models are (Generative Adverserial Networks) GAN’s and (Variational Auto Encoders)VAE’s.