Face editing with GAN -- A Review

TL;DR

This review paper discusses the evolution, improvements, and applications of GANs, emphasizing their ability to generate realistic content across various fields like image and music synthesis.

Contribution

It provides a comprehensive overview of GAN development, key improvements, and comparative analysis of different models, highlighting recent advancements in the field.

Findings

GANs can generate highly realistic data content.

Recent improvements enhance training stability and output quality.

GANs are widely applicable in image and music synthesis.

Abstract

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.