User:Shemati

Introduction

Generative adversarial networks (GANs) are one of the most important generative models, where a couple of discriminator and generator compete to each other to solve a minimax game. Based on the original GAN paper, when the training is finished and Nash Equilibrium is reached, the discriminator is nothing but a constant function that assigns a score of 0.5 everywhere. This means that in this setting discriminator is nothing more than a tool to train the generator. Furthermore, the generator in traditional GAN model the data density in an implicit manner while in some applications we need to have an explicit generative model of data. Recently, it has been shown that training an energy-based model (EBM) with a parameterized variational is also a similar minimax game similar to the one in GAN. Although they are similar, There is an advantage of this EBM view that is unlike the original GAN formulation, in this EBM model discriminator itself is an explicit density model of the data.

Considering some remarks, authors in this paper show that an energy-based model can be trained using similar minmax formulation in GANs. After training the energy based model, the use Fisher Score and Fisher Information (which are calculated based on derivative of the generative models w.r.t its parameters) to evaluate the power of discriminator in modelling data distribution. More precisely, they calculate normalized Fisher Vectors and Fisher Distance measure using the derivative of the discriminator to estimate similarities both between individual data samples and between sets of samples. They name these derived representations Adversarial Fisher Vectors (AFVs). before continue one may argue why should we