Adversarial Networks

Adversarial networks, specifically Generative Adversarial Networks (GANs), are a type of machine learning model that consists of two neural networks working against each other. One network, the generator, creates new data that resembles real examples (like realistic images). The other, the discriminator, evaluates whether the data is real or fake. The generator aims to produce increasingly convincing data to fool the discriminator, while the discriminator improves at spotting fakes. This competitive process allows GANs to generate highly realistic data, making them useful for applications like image synthesis, data augmentation, and artistic creation.