GAN was first introduced by Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio in their paper, Generative Adversarial Networks, in 2014.

GANs are used extensively for generating new data points. They can be applied to any type of dataset, but they are popularly used for generating images. Some of the applications of GANs include generating realistic human faces, converting grayscale images to colored images, translating text descriptions into realistic images, and many more.

Yann LeCun said the following about GANs:

GANs have evolved so much in recent years that they can generate a very realistic image. The following figure shows the evolution of GANs in generating images over the course of five years:

Excited about GANs already? Now, we will see how exactly they work. Before going ahead, let's consider a simple analogy. Let's say you are the police and your task is to find counterfeit money, and the role of the counterfeiter is to create fake money and cheat the police.

The counterfeiter constantly tries to create fake money in a way that is so realistic that it cannot be differentiated from the real money. But the police have to identify whether the money is real or fake. So, the counterfeiter and the police essentially play a two-player game where one tries to defeat the other. GANs work something like this. They consist of two important components:

• Generator
• Discriminator

You can perceive the generator as analogous to the counterfeiter, while the discriminator is analogous to the police. That is, the role of the generator is to create fake money, and the role of the discriminator is to identify whether the money is fake or real.

Without going into detail, first, we will get a basic understanding of GANs. Let's say we want our GAN to generate handwritten digits. How can we do that? First, we will take a dataset containing a collection of handwritten digits; say, the MNIST dataset. The generator learns the distribution of images in our dataset. Thus, it learns the distribution of handwritten digits in our training set. Once, it learns the distribution of the images in our dataset, and we feed a random noise to the generator, it will convert the random noise into a new handwritten digit similar to the one in our training set based on the learned distribution:

The goal of the discriminator is to perform a classification task. Given an image, it classifies it as real or fake; that is, whether the image is from the training set or the image is generated by the generator:

The generator component of GAN is basically a generative model, and the discriminator component is basically a discriminative model. Thus, the generator learns the distribution of the class and the discriminator learns the decision boundary of a class.

As shown in the following figure, we feed a random noise to the generator, and it then converts this random noise into a new image similar to the one we have in our training set, but not exactly the same as the images in the training set. The image generated by the generator is called a fake image, and the images in our training set are called real images. We feed both the real and fake images to the discriminator, which tells us the probability of them being real. It returns 0 if the image is fake and 1 if the image is real:

Now that we have a basic understanding of generators and discriminators, we will study each of the components in detail.