What is a Generative Adversarial Network(GAN)?
Generative Adversarial Networks (GANs): An Overview Generative Adversarial Networks (GANs) are a type of deep learning algorithm that are used to generate new data that is similar to a given dataset. GANs are made up of two neural networks, the generator and the discriminator, that compete with each other in a game-like setting. The generator network is responsible for creating new data samples, while the discriminator network is trained to distinguish between the real data and the generated data. The two networks are trained together, with the generator trying to create samples that can fool the discriminator, and the discriminator trying to correctly identify the real samples from the fake ones. The GAN architecture was introduced by Ian Goodfellow in 2014, and has since become one of the most popular deep learning models for image and video synthesis. GANs have been used to create realistic images of celebrities, generate realistic video game characters, and even to create art...