Generative Adversarial Network • GAN

A Generative Adversarial Network, or GAN, is a type of artificial intelligence algorithm used for generating new data. The algorithm consists of two components: a generator and a discriminator. The generator creates new data, while the discriminator evaluates the data to determine whether it is real or fake. The two components are trained against each other, with the generator trying to create data that will fool the discriminator and the discriminator trying to become better at identifying fake or “bad” data. As they train against each other, they both become more efficient at their respective tasks, which results in the generation of higher-quality data. GANs have been used for applications such as image generation, text generation, and 3D object generation. They have also been used for more practical applications such as creating new drugs and improving medical images.