Results 291 to 300 of about 124,232 (320)

Adapting a style based generative adversarial network to create images depicting cleft lip deformity. [PDF]

Sci Rep
Hayajneh A, Serpedin E, Shaqfeh M, Glass G, Stotland MA.   +4 more
europepmc   +1 more source

Detracking Autoencoding Conditional Generative Adversarial Network: Improved Generative Adversarial Network Method for Tabular Missing Value Imputation. [PDF]

Entropy (Basel)
Liu J, Duan Z, Hu X, Zhong J, Yin Y.
europepmc   +1 more source

Optimized customer churn prediction using tabular generative adversarial network (GAN)-based hybrid sampling method and cost-sensitive learning. [PDF]

PeerJ Comput Sci
Adiputra INM, Wanchai P, Lin PC.
europepmc   +1 more source

Using generative adversarial network to improve the accuracy of detecting AI-generated tweets. [PDF]

Sci Rep
Hui Y.
europepmc   +1 more source

A generative adversarial network-based accurate masked face recognition model using dual scale adaptive efficient attention network. [PDF]

Sci Rep
Alzubi JA, Pokkuluri KS, Arunachalam R, Shukla SK, Venugopal S, Arunachalam K.   +5 more
europepmc   +1 more source

Virtual try-on with generative adversarial networks

, 2019
openaire   +1 more source

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Generative Adversarial Networks in Cardiology

Canadian Journal of Cardiology, 2022
Generative adversarial networks (GANs) are state-of-the-art neural network models used to synthesise images and other data. GANs brought a considerable improvement to the quality of synthetic data, quickly becoming the standard for data-generation tasks.
Skandarani, Youssef, Lalande, Alain, Afilalo, Jonathan, Jodoin, Pierre-Marc   +3 more
openaire   +4 more sources

Generative Adversarial Networks

2018
For many AI projects, deep learning techniques are increasingly being used as the building blocks for innovative solutions ranging from image classification to object detection, image segmentation, image similarity, and text analytics (e.g., sentiment analysis, key phrase extraction). GANs, first introduced by Goodfellow et al.
Mathew Salvaris, Danielle Dean, Wee Hyong Tok   +2 more
openaire   +2 more sources

Generative Adversarial Network

2020
Generative adversarial networks (GANs) are a type of deep learning model designed by Ian Goodfellow and his colleagues in 2014.
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Enhancing generative adversarial network

Global Journal of Engineering and Technology Advances
The paper provides a comprehensive review of various GAN methods from the perspectives of theory, and applications. GAN algorithms' mathematical representations, and structures are detailed. The commonalities and differences among these GANs methods are compared.
Rajbeer Kaur, Amanpreet Kaur, Kirandeep Kaur   +2 more
openaire   +1 more source