Generative Adversarial Networks $($GANs$)$ can be broken down into three parts:

Generative: To learn a generative model, which describes how data is generated in terms of a probabilistic model.

Adversarial: The training of a model is done in an adversarial setting.

Networks: Use of deep neural networks as the artificial intelligence $($AI$)$ algorithms for training purposes.

In GANs, there is a Generator and a Discriminator. The Generator generates fake samples of data $($an image, audio, etc.$)$ and tries to fool the Discriminator. The Discriminator, on the other hand, tries to distinguish between the real and fake samples. The Generator and the Discriminator are both neural networks, and they both run in competition with each other in the training phase. The steps are repeated several times, and after each repetition, the Generator and Discriminator get better and better in their respective jobs.

Training a GAN has two parts:

A: The Discriminator is trained while the Generator is idle. In this phase, the network is only forward propagated and no back$-$propagation is done. The Discriminator is trained on real data for n epochs to see if it can correctly predict them as real. Also, in this phase, the Discriminator is trained on the fake generated data from the Generator to see if it can correctly predict them as fake.

B: The Generator is trained while the Discriminator is idle. After the Discriminator is trained by the generated fake data of the Generator, we can get its predictions and use the results to train the Generator and improve from the previous state to try and fool the Discriminator.

The above method is repeated for a few epochs, and then, we manually check the fake data if it seems genuine. If it seems acceptable, then the training is stopped; otherwise, it$$s allowed to continue for a few more epochs.