Deep Learning - Deep Convolutional Generative Adversarial Networks Basics Vinod Sharma's Blog

I introduced the basic analogy, concept, and ideas behind "How GANs work". This post will do a little bit of a deep dive. Generative Adversarial Networks are a class of algorithms used in the unsupervised learning environment. As the name suggests they are called Adversarial Networks because they are is made up of two competing neural networks. Both networks compete with each other to achieve a zero-sum game. Both neural networks are assigned different job role i.e. contesting with each other. The process in GANs involves automatically learning to discover the regularities or patterns in input data.