What is the goal of the Generator in a GAN?

The goal of the Generator in a Generative Adversarial Network (GAN) is to produce data that is indistinguishable from real data. This is achieved by learning the underlying distribution of the training data and generating new samples that closely resemble those real samples. The Generator operates in opposition to the Discriminator, which tries to distinguish between real and generated data.

In this adversarial setup, the Generator continuously improves its output through feedback from the Discriminator. When the Generator successfully creates data that the Discriminator cannot reliably identify as fake, it means the Generator has effectively learned to mimic the characteristics of the real data distribution. This continuous interaction drives both components of the GAN to enhance their respective performances, with the ultimate objective of the Generator being to create high-quality, realistic data that can fool the Discriminator.