An Easy Introduction to Generative Adversarial Networks in Deep Learning

Generative Adversarial Networks (GANs) are classified within the group of generative models. That means that they're able to produce, i.e generate completely new, "valid" data. By valid data, we mean that the network's output should be something that we would deem acceptable for our target. To illustrate, consider an example where we wish to generate some new images for training an image classification network. Of course, for such an application we want our training data to be as realistic as possible, perhaps quite similar in style to other image classification training data sets.