Generative Adversarial Networks (GAN) were invented by Ian Goodfellow, in 2014. It is an unsupervised algorithm where two neural networks are trained as a discriminator and a generator, simultaneously. The technique can generate an image from random noise and a discriminator can evaluate whether is an original image. After further training, the generator network can generate photo-realistic images. The generator network is typically a deconvolutional neural network and the discriminator is a convolution neural network.
An excellent analogy to understand this is to think of the generator as someone who prints fake money and the discriminator as a police officer who determines whether the money is fake or not. The generator keeps improving the quality of the fake money based on the feedback from the police till the police can't differentiate between the original and fake money. Now, let's start with the implementation.
