Generator and Discriminator consist of Deconvolutional Network (DNN) and Convolutional Neural Network (CNN). CNN is a neural network which encodes the hundreds of pixels of an image into a vector of small dimensions (z) which is a summary of the image. DNN is a network that learns filters to recover the original image from z. When a real image is given, Discriminator should output 1 or 0 for whether the image was generated from Generator. In the contrast, Generator generates an image from z, which follows a Gaussian Distribution, and tries to figure out the distribution of human images from z.

We study in this paper the rate of convergence for learning distributions with the Generative Adversarial Networks (GAN) framework, which subsumes Wasserstein, Sobolev and MMD GANs as special cases. We study a wide range of parametric and nonparametric target distributions, under a collection of objective evaluation metrics. On the nonparametric end, we investigate the minimax optimal rates and fundamental difficulty of the density estimation under the adversarial framework. On the parametric end, we establish theory for neural network classes, that characterizes the interplay between the choice of generator and discriminator. We investigate how to improve the GAN framework with better theoretical guarantee through the lens of regularization. We discover and isolate a new notion of regularization, called the \textit{generator/discriminator pair regularization}, that sheds light on the advantage of GAN compared to classic parametric and nonparametric approaches for density estimation.

GAN is an unsupervised deep learning algorithm where we have a Generator pitted against an adversarial network called Discriminator. Discriminators are a team of cops trying to detect the counterfeit currency. Counterfeiters and cops both are trying to beat each other at their game. Generator's objective will be to generate data that is very similar to the training data. Data generated from Generator should be indistinguishable from the real data.

In a world filled with technology and artificial intelligence, it is becoming increasingly harder to distinguish between what is real and what is fake. Look at these two pictures below. Can you tell which one is a real-life photograph and which one is created by artificial intelligence? The crazy thing is that both of these images are actually fake, created by NVIDIA's new hyperrealistic face generator, which uses an algorithmic architecture called a generative adversarial network (GANs). Researching more into GANs and their applications in today's society, I found that they can be used everywhere, from text to image generation to even predicting the next frame in a video!

The purpose of this article series is to provide an overview of GAN research and explain the nature of the contributions. I'm new to this area myself, so this will surely be incomplete, but hopefully it can provide some quick context to other newbies. For Part I we'll introduce GANs at a high level and summarize the original paper. It's assumed you're familiar with the basics of neural networks. What is meant by generative?