Facial expression synthesis based on similar faces

Testa, Rafael Luiz; Machado‐Lima, Ariane; Nunes, Fátima L. S.

doi:10.1007/s11042-021-11525-4

Cited by 2 publications

(1 citation statement)

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“…For instance, GMM‐score (Harshavardha et al, 2022) evaluates the generated images based on a package of different criteria, including inter‐class and intra‐class diversity, sample quality, and other metrics like precision, recall, and F1 score. Also, Testa et al (2021) proposed an evaluation metric to evaluate the quality of the synthesized facial images by comparing the common areas of the facial features in the generated and reference facial images. Also, the comprehensive contour index metric (CCIM; B. Zhang, Gu, et al, 2022) was proposed as an evaluation metric for blurred and low illumination image reconstruction tasks.…”

Section: Gan Evaluation Metricsmentioning

confidence: 99%

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

Mohammed

2023

WIREs Computational Stats

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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

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Section: Gan Evaluation Metricsmentioning

confidence: 99%