Ever since its inception, GAN has been the talk of the town among Machine Learning Practitioners and Researchers .It’s been described as the algorithmic philosophy which is going to drive the future innovation and growth in deep learning.

In statistical learning i.e. “learning from data approach” we deal with two types of models:

Discriminative Model

Generative Model

Classification and Regression are the two prime examples of Discriminative tasks where we have a training set and our model’s job is to make categorical or continuous value predictions i.e E(y|x),on the other hand with Generative model we want to generate a new sample of data which is identical in distribution with train data.In traditional statistical sense GAN can be labelled as two-sample hypothesis test but in practice GAN is more constructive and creative in approach.

Gan is the brainchild of prominent deep learning scientist Ian Goodfellow and his research colleagues. In Ian’s words:

A framework for estimating generative models via an adversarial process,in which we simultaneously train two models: generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came form training data rather than G.This framework corresponds to minimax two players game.