Generative adversarial networks(GANs) took the Machine Learning field by storm last year with those impressive fake human-like faces.

Bonus Point* They are basically generated from nothing.

Irrefutably, GANs implements implicit learning methods where the model learns without the data directly passing through the network, unlike those explicit techniques where weights are learned directly from the data.

Intuition

Okay, suppose in the city of Rio de Janeiro, money forging felonies are increasing so a department is appointed to check in these cases. Detectives are expected to classify the legit ones and fake ones. When the officers correctly classify fake currency they are appreciated and when they make mistakes, feedback is provided by Central Bank. Now, whenever fake currencies are busted, forgers aims at circulating better fake currencies in the market. This is the recursive process when detectives identify fake currencies, the forger learns a better way to develop a more authentic currency, similarly with the admission of more fake currencies in the market the detectives develop an even better strategy to identify fabricated currencies. Both detectives and forgers will become better at what they do by learning from each other.

This is the basic intuition behind GANs.

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