Face editing with GAN – A Review

by   Parthak Mehta, et al.

In recent years, Generative Adversarial Networks (GANs) have become a hot topic among researchers and engineers that work with deep learning. It has been a ground-breaking technique which can generate new pieces of content of data in a consistent way. The topic of GANs has exploded in popularity due to its applicability in fields like image generation and synthesis, and music production and composition. GANs have two competing neural networks: a generator and a discriminator. The generator is used to produce new samples or pieces of content, while the discriminator is used to recognize whether the piece of content is real or generated. What makes it different from other generative models is its ability to learn unlabeled samples. In this review paper, we will discuss the evolution of GANs, several improvements proposed by the authors and a brief comparison between the different models. Index Terms generative adversarial networks, unsupervised learning, deep learning.


Procedural content generation of puzzle games using conditional generative adversarial networks

In this article, we present an experimental approach to using parameteri...

NFTGAN: Non-Fungible Token Art Generation Using Generative Adversarial Networks

Digital arts have gained an unprecedented level of popularity with the e...

Gradient Estimators for Implicit Models

Implicit models, which allow for the generation of samples but not for p...

Redes Generativas Adversarias (GAN) Fundamentos Teóricos y Aplicaciones

Generative adversarial networks (GANs) are a method based on the trainin...

LogicGAN: Logic-guided Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a revolutionary class of Deep...

Decentralized Learning of Generative Adversarial Networks from Multi-Client Non-iid Data

This work addresses a new problem of learning generative adversarial net...

Please sign up or login with your details

Forgot password? Click here to reset