Results 1 to 10 of about 115,058 (253)

An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs

Applied Sciences, 2020
Medical image segmentation is a classic challenging problem. The segmentation of parts of interest in cardiac medical images is a basic task for cardiac image diagnosis and guided surgery.
Xiaochang Wu, Xiaolin Tian
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Generative Adversarial Networks in Speech Enhancement: A Survey

IEEE Access
Generative adversarial networks are a powerful type of model in deep learning. They have been successfully applied within different domains. This review focuses on the usage of generative adversarial networks for speech enhancement.
Justina Ramonaite, Grazina Korvel, Gintautas Tamulevicius   +2 more
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Attribute-Aware Generative Design With Generative Adversarial Networks

IEEE Access, 2020
The designers' tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often limit their ability to innovate during the design ideation process.
Chenxi Yuan, Mohsen Moghaddam
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Study of image reconstruction efficiency in a single-pixel imaging method using generative adversarial networks

Компьютерная оптика
Single-pixel imaging is a promising image acquisition method that provides an alternative to traditional imaging methods using multi-pixel matrices. However, algorithmic image reconstruction from measurements of a single-pixel camera is a non-trivial ...
D.V. Babukhin, A.A. Reutov, D.V. Sych
doaj   +1 more source

Review of Application of Generative Adversarial Networks in Image Restoration [PDF]

Jisuanji kexue yu tansuo
With the rapid development of generative adversarial networks, many image restoration problems that are difficult to solve based on traditional methods have gained new research approaches.
GONG Ying, XU Wentao, ZHAO Ce, WANG Binjun
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Generative Adversarial Networks GAN Overview

Jisuanji kexue yu tansuo, 2020
As a new unsupervised learning algorithm framework, generative adversarial networks (GAN) has been favored by more and more researchers, and it has become a research hotspot. GAN is inspired by the two-person zero-sum game theory in game theory.
LIANG Junjie, WEI Jianjing, JIANG Zhengfeng
doaj   +1 more source

Phylogenetic inference using Generative Adversarial Networks

Bioinformatics, 2022
AbstractMotivationThe application of machine learning approaches in phylogenetics has been impeded by the vast model space associated with inference. Supervised machine learning approaches require data from across this space to train models. Because of this, previous approaches have typically been limited to inferring relationships among unrooted ...
Megan L. Smith, Matthew W. Hahn
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Generating Adversarial Examples with Adversarial Networks [PDF]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high ...
Xiao, Chaowei, Li, Bo, Zhu, Jun-Yan, He, Warren, Liu, Mingyan, Song, Dawn   +5 more
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BaMSGAN: Self-Attention Generative Adversarial Network with Blur and Memory for Anime Face Generation

Mathematics, 2023
In this paper, we propose a novel network, self-attention generative adversarial network with blur and memory (BaMSGAN), for generating anime faces with improved clarity and faster convergence while retaining the capacity for continuous learning ...
Xu Li, Bowei Li, Minghao Fang, Rui Huang, Xiaoran Huang   +4 more
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Generative Adversarial Networks: Recent Developments [PDF]

, 2019
10 ...
Zamorski, Maciej, Zdobylak, Adrian, Zięba, Maciej, Świątek, Jerzy   +3 more
openaire   +2 more sources