Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. GANs consist of two neural networks, the generator and the discriminator, which are trained simultaneously through adversarial processes.

• Generator: This network generates new data instances that resemble the training data. It takes random noise as input and produces data that mimics the real data distribution.

• Discriminator: This network evaluates the data instances. It distinguishes between real data (from the training set) and fake data (produced by the generator).

The training process involves the generator trying to produce data that can fool the discriminator, while the discriminator tries to correctly identify real versus fake data. This adversarial process continues until the generator produces data that is indistinguishable from the real data, according to the discriminator.

GANs have been widely used in various applications, including image generation, video generation, and even in creating realistic deepfakes. They are known for their ability to generate high-quality, realistic data.