Generative Adversarial Network: A simpler intuitive explanation. Let us assume Sergio is planning his next big heist and he needs his team to travel to a foreign country.
Let us assume Sergio is planning his next big heist and he needs his team to travel to a foreign country. Owing to the nature of his job he has decided to generate _fake passports. He doesn’t want to take any chances because he believes that the passport officer can _discriminate _between original and fake passports. He has decided to use his _network of designers and experts to carry out his plans effectively.
Generative Adversarial Networks have two components a generator and a discriminator. Just take my words as of now :) Let's get back to Sergio's plan and problems.
The diagram below represents Sergio’s plan of action in which a fake passport is generated by using the combined skills of the professionals.
Sergio’s generator network (Image by Author using ML-Visuals).
If we were to generalise Sergio’s plan we can say, the diagram is a _generator _of fake passports. A network of professionals who essentially know the security features of a passports try to recreate a similar one by using their knowledge. In other words given a space the goal is to draw an example which resembles the original space.
This is precisely what the _generator _component of a GAN does. It feeds upon the real world examples to observe their features and then create a new example which resembles the original examples.
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