GAN

Generative adversarial network is used to generate image from an input vector of random value. GAN comprises of 2 neural networks - genarator used to create innate and a discriminator used to rate how real the image generated is. 

The image created by generator is fed to the discriminator with real images. The output from the discriminator s used to train the generator so that it eventually generate image that the discriminator could not differentiate form real ones.  Training us via back propagation to adjust the weighs and bias  

Once the model is grained, the discriminator is discarded. The generator is used to generate artificial images