What role does the Generator play in a GAN?

The Generator in a Generative Adversarial Network (GAN) is responsible for creating fake data samples that mimic real data. Its primary function is to learn the distribution of the real data and generate new samples that are indistinguishable from authentic data. This involves taking random noise as input and transforming it through a process of learned patterns and features, resulting in output that appears similar to the training data.

This process is fundamental in the adversarial setup of GANs, where the Generator competes against a Discriminator. The Discriminator's role is to differentiate between real and generated (fake) data, and through this competition, the Generator improves over time, enhancing its ability to produce more realistic samples. The success of the Generator is measured by how well it can deceive the Discriminator, thereby highlighting the importance of generating high-quality synthetic data that resembles the actual data distribution.