Overview of GAN Structure
Source of Publication
SpringerBriefs in Computer Science
This chapter will introduce generative adversarial networks (GANs), a type of neural network that can generate new data samples that resemble a given dataset. The chapter will discuss the difference between generative and discriminative models, an overview of the basic architecture of GANs, which consists of a generator and a discriminator, and how they are trained using an adversarial process. We also discuss the weaknesses of GANs and highlight some of their current challenges and limitations.
Springer International Publishing
Convolutional neural network, Discriminative model, Fake data, Generative model, Loss function, Real data
Kaddoura, Sanaa, "Overview of GAN Structure" (2023). All Works. 5911.
Indexed in Scopus