GAN training

Generative Adversarial Networks (GANs) involve two neural networks working together: a generator and a discriminator. The generator creates new data resembling real data, while the discriminator evaluates whether the data is real or fake. They compete: the generator tries to produce convincing data, and the discriminator tries to spot fakes. Over time, they improve simultaneously—leading the generator to produce increasingly realistic outputs. This adversarial process refines the generator’s ability to create data that closely mimics real-world examples, such as images or sounds.