GenerativeAdversarialNetworkForschung

Generative Adversarial Networks, often abbreviated as GANs, are a class of machine learning frameworks. They were introduced by Ian Goodfellow and his colleagues in 2014. GANs consist of two neural networks, a generator and a discriminator, that are trained simultaneously in a zero-sum game.

The generator's objective is to create new data instances that resemble the training data. It takes random noise as input and transforms it into a data sample, such as an image or text. The discriminator's role is to distinguish between real data instances from the training set and fake instances produced by the generator.

During training, the generator attempts to produce outputs that can fool the discriminator, while the discriminator tries to become better at identifying the generator's fakes. This adversarial process drives both networks to improve. The generator learns to produce increasingly realistic data, and the discriminator becomes more adept at detecting subtle differences.

GANs have a wide range of applications. They are used for generating realistic images, creating synthetic data for training other machine learning models, image-to-image translation, super-resolution, and even in artistic endeavors. The core idea is to leverage the competitive nature of the two networks to learn complex data distributions and generate novel, high-quality content.