1 - 03 Generative Adversarial Networks: How GANs Work

We offer an overview of Adversary Generative Networks (GAN), a type of machine learning algorithm that uses an adversarial learning framework with two submodules: a generator and a discriminator. The fundamental concept of GANs is explained with an analogy of a counterfeiter and the police, and generative modeling is deepened, highlighting the problem of intractable normalization constants and how GANs address it. It also examines the loss function used to train GANs, its relationship with zero-sum or minimax play, and common training problems, such as mode collapse and gradients that fade. In addition, the adversarial nature of GANs is described and their uses in image generation, video frame prediction and image improvement are highlighted.