A generative adversarial network (GAN) is a type of machine learning algorithm that is used for generating new data that is similar to a given dataset. It was introduced by Ian Goodfellow in 2014.
The GAN consists of two neural networks: the generator and the discriminator. The generator network creates new data samples, while the discriminator network evaluates whether these samples are real or fake.
The generator network is trained to create realistic samples that can fool the discriminator into believing that they are real. The discriminator network is trained to correctly identify which samples are real and which ones are generated by the generator. The two networks are trained simultaneously, with the generator trying to create better and better samples while the discriminator tries to distinguish between real and generated samples.
The training process continues until the generator is able to create samples that are so realistic that the discriminator cannot distinguish between them and real samples. At this point, the generator has learned to create data that is very similar to the original dataset.
GANs have been used to generate realistic images, video, and audio data. They can also be used for data augmentation, which involves creating new samples from existing ones to increase the size of the dataset. Additionally, GANs have been used in other fields such as text generation, drug discovery, and game development.
