How can generative adversarial networks learn real-life distributions easily
A Generative adversarial network, or GAN, is one of the most powerful machine learning models proposed by Goodfellow et al. for learning to generate samples from complicated real-world distributions. GANs have sparked millions of applications, ranging from generating realistic images or cartoon characters to text-to-image translations. Turing award laureate Yann LeCun called GANs “the most interesting idea in the last 10 years in ML.”
In the context of generating images, GANs consist of two parts. 1) A parameterized (deconvolutional) generator network (G) that takes input (z) which is a random Gaussian vector and outputs a fake image (G(z)). 2) A parameterized (convolutional) discriminator network (D) that takes as input an image (X)and outputs a real value (D(X)). To learn a target distribution