Generative adversarial networks (GANs) are a deep-learning model first described by Ian Goodfellow in 2014. GANs use two neural networks – one that creates content and one that analyses it – in a pseudo-game-like adversarial process. According to Goodfellow's counterfeiter analogy, the generative model can be thought of as analogous to a team of counterfeiters, trying to produce fake currency and use it without detection, while the discriminative model is analogous to the police, trying to detect counterfeit currency. Competition in this game drives both teams to improve their methods until the counterfeits are indistinguishable from the genuine articles.

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