Generative Adversarial Networks (GANs) are a class of neural networks designed for generating new data instances that resemble a given training dataset. Introduced in 2014 by Ian Goodfellow and his colleagues, GANs have gained significant popularity in the field of deep learning due to their innovative approach to generative modeling ³⁵.

A GAN consists of two main components: a generator and a discriminator. The generator’s role is to create synthetic data that mimics the real data, while the discriminator’s task is to differentiate between real and fake data. This setup creates an adversarial process where the generator aims to fool the discriminator, and the discriminator strives to correctly identify the authenticity of the data it receives ¹²⁵.

The training process involves alternating between training the generator and the discriminator. The generator improves its output based on feedback from the discriminator, which penalizes it for producing fake data. This dynamic continues until the generator produces data that is indistinguishable from real data, effectively capturing the underlying distribution of the training dataset ¹⁴⁵.