Neural Network Programming with TensorFlow

GANs

GANs were introduced by a group of researchers at the University of Montreal led by Ian Goodfellow. The core idea behind a GAN model is to have two competing neural network models. One network takes the noise as input and generates samples (hence known as generator). The second model (known as discriminator) gets samples from both the generator and the actual training data, and should be able to differentiate between the two sources. Generative and discriminative networks are playing a continuous game, where the generator model is learning to generate more realistic samples or examples, and the discriminator is learning to get better and better at differentiating generated data from the real data. The two networks are trained simultaneously, ...