Generative Adversarial Network (GAN)

Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed to generate new data samples that resemble a given dataset. The architecture consists of two main components: the generator, which creates new instances of data, and the discriminator, which evaluates the authenticity of the generated instances against real data. This adversarial process continues until the generator produces high-quality data that the discriminator can no longer distinguish from actual data.