Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments

by   Pegah Salehi, et al.

One of the most significant challenges in statistical signal processing and machine learning is how to obtain a generative model that can produce samples of large-scale data distribution, such as images and speeches. Generative Adversarial Network (GAN) is an effective method to address this problem. The GANs provide an appropriate way to learn deep representations without widespread use of labeled training data. This approach has attracted the attention of many researchers in computer vision since it can generate a large amount of data without precise modeling of the probability density function (PDF). In GANs, the generative model is estimated via a competitive process where the generator and discriminator networks are trained simultaneously. The generator learns to generate plausible data, and the discriminator learns to distinguish fake data created by the generator from real data samples. Given the rapid growth of GANs over the last few years and their application in various fields, it is necessary to investigate these networks accurately. In this paper, after introducing the main concepts and the theory of GAN, two new deep generative models are compared, the evaluation metrics utilized in the literature and challenges of GANs are also explained. Moreover, the most remarkable GAN architectures are categorized and discussed. Finally, the essential applications in computer vision are examined.


page 1

page 2

page 3

page 6

page 10

page 12

page 13

page 14


Triple Generative Adversarial Networks

Generative adversarial networks (GANs) have shown promise in image gener...

Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey Evaluation

Despite the recency of their conception, Generative Adversarial Networks...

Face editing with GAN – A Review

In recent years, Generative Adversarial Networks (GANs) have become a ho...

On distinguishability criteria for estimating generative models

Two recently introduced criteria for estimation of generative models are...

Generative Adversarial Neural Operators

We propose the generative adversarial neural operator (GANO), a generati...

Influence Estimation for Generative Adversarial Networks

Identifying harmful instances, whose absence in a training dataset impro...

Automatic DJ Transitions with Differentiable Audio Effects and Generative Adversarial Networks

A central task of a Disc Jockey (DJ) is to create a mixset of mu-sic wit...