Generative adversarial networks
Generative adversarial networks (GANs) are another form of deep neural network architecture, and is a combination of two networks that compete and cooperate with each other. It was introduced by Ian Goodfellow and Yoshua Bengio in 2014.
GANs can learn to mimic any distribution of data, which ideally means that GANs can be taught to create an object that's similar to an existing one in any domain, such as images, music, speech, and prose. It can create photos of any object that has never existed before. They are robot artists in a sense, and their output is impressive.
It falls under unsupervised learning wherein both of the networks learn their task upon training. One of the networks is called the generator and the other is called the discriminator.
To make this more understandable, we can think of a GAN as a case of a counterfeiter (generator) and a cop (discriminator). At the outset, the counterfeiter shows the cop fake money. The cop works like a detective...
