[Deep learning] Introduction of Generative Adversarial Networks (GANs)

Generative adversarial networks (GANs), formed in 2014 [1], is a state of the art deep neural network with many applications. Unlike the traditional machine learning in unsupervised learning (it does not require a targeted label), GANs is a generative model which generates new content by given data. The analogy of GANs is known as a fake-currency detection game between a counterfeiter and police [1]. According to the tutorial of GANs by Goodfellow [2], GANs consists of two characters, namely, the generator (counterfeiter) and the discriminator (police). The counterfeiter tries to produce fake money and deceive the police (discriminator) by looking at the real banknote.