Facial Image Denoising and Reconstruction

Vartak, Sahil; Wad, Bhagyashree; Varandekar, Pratik; Kapse, Dnyaneshwar

doi:10.1007/978-981-16-5301-8_1

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, PIC-GAN (Lv et al, 2021) proposed to reconstruct the MRI images automatically to mitigate some issues related to the physical environment of capturing these images and affect their accuracy. On the other hand, Vartak et al (2021)proposed a GAN model to remove the noise from the facial images.…”

Section: Image Denoisingmentioning

confidence: 99%

A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

Mohammed

2023

WIREs Computational Stats

View full text Add to dashboard Cite

In machine learning, a generative model is responsible for generating new samples of data in terms of a probabilistic model. Generative adversarial network (GAN) has been widely used to generate realistic samples in different domains and outperforms its peers in the generative models family. However, producing a robust GAN model is not a trivial task because many challenges face the GAN during the training process and impact its performance, affecting the quality and diversity of the generated samples. In this article, we conduct a comprehensive review of GANs to present the fundamentals of GAN, including its components, types, and objective functions. Also, we present an overview of the evaluation matrices used to evaluate GAN models. Moreover, we list the applications of GANs and research work in various domains. Finally, we present the challenges that face GANs and highlight two significant issues, representing mode collapse and training instability, in addition to those research efforts that tackle these challenges.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Deep Learning Statistical Learning and Exploratory Methods of the Data Sciences > Neural Networks

show abstract

Section: Image Denoisingmentioning

confidence: 99%

A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

Mohammed

2023

WIREs Computational Stats

View full text Add to dashboard Cite

show abstract

Facial Image Denoising and Reconstruction

Cited by 1 publication

References 8 publications

A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

Contact Info

Product

Resources

About