This APPROACH can be understood with the FAMOUS example of the wine seller. Let us say that there is a wine seller who has his own shop. This wine seller purchases wine from the dealers who sell him the wine at a low cost so that he can sell the wine at a high cost to the customers. Now, let us say that the dealers whom he is purchasing the wine from, are SELLING him fake wine. They do this as the fake wine costs way less than the original wine and the fake and the real wine are indistinguishable to a normal CONSUMER (customer in this case). The shop owner has some friends who are wine experts and he sends his wine to them every time before keeping the stock for sale in his shop. So, his friends, the wine experts, give him feedback that the wine is probably fake. Since the wine seller has been purchasing the wine for a long time from the same dealers, he wants to make sure that their feedback is right before he complains to the dealers about it. Now, let us say that the dealers also have got a tip from somewhere that the wine seller is suspicious of them.

So, in this situation, the dealers will try their best to sell the fake wine WHEREAS the wine seller will try his best to identify the fake wine. Let us see this with the help of a diagram shown below:

From the image above, it is clear that a noise vector is entering the generator (dealer) and he generates the fake wine and the discriminator has to distinguish between the fake wine and real wine. This is a Generative Adversarial Network (GAN).

In a GAN, there are 2 main components viz. Generator and Discrminator. So, the generator is a CNN that keeps producing images and the discriminator tries to identify the real images from the fake ones.