A Generative Adversarial Network (GAN) comprises two main components:
Generator: This component creates synthetic data. It takes random noise as input and transforms it into data that resembles the training dataset.
Discriminator: The discriminator’s role is to distinguish between real and generated data. It learns to classify data as real or fake.
GANs operate on a feedback loop. The generator aims to produce data that can fool the discriminator, while the discriminator gets better at distinguishing real from fake data. This competition results in the generation of high-quality synthetic content.
