Results 41 to 50 of about 118,423 (171)

Image Super-Resolution Reconstruction Algorithm Based on Generative Adversarial Networks [PDF]

Jisuanji gongcheng, 2021
The existing image Super-Resolution (SR) reconstruction algorithms have difficulty in network training and cause artifacts in the generated images.To address the problem,this paper proposes a SR reconstruction algorithm based on Generative Adversarial ...
JIANG Yuning, LI Jinhua, ZHAO Junli
doaj   +1 more source

Lung image segmentation via generative adversarial networks

Frontiers in Physiology
IntroductionLung image segmentation plays an important role in computer-aid pulmonary disease diagnosis and treatment.MethodsThis paper explores the lung CT image segmentation method by generative adversarial networks.
Jiaxin Cai, Hongfeng Zhu, Siyu Liu, Yang Qi, Rongshang Chen   +4 more
doaj   +1 more source

Annealed Generative Adversarial Networks

CoRR, 2017
9 pages, 6 ...
Arash Mehrjou, Bernhard Schölkopf, Saeed Saremi   +2 more
openaire   +2 more sources

Stacked Generative Adversarial Networks [PDF]

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
CVPR 2017, camera-ready ...
Xun Huang 0002, Yixuan Li 0001, Omid Poursaeed, John E. Hopcroft, Serge J. Belongie   +4 more
openaire   +2 more sources

Recent Generative Adversarial Approach in Face Aging and Dataset Review

IEEE Access, 2022
Many studies have been conducted in the field of face aging, from approaches that use pure image-processing algorithms, to those that use generative adversarial networks.
Hady Pranoto, Yaya Heryadi, Harco Leslie Hendric Spits Warnars, Widodo Budiharto   +3 more
doaj   +1 more source

Wasserstein Introspective Neural Networks

, 2018
We present Wasserstein introspective neural networks (WINN) that are both a generator and a discriminator within a single model. WINN provides a significant improvement over the recent introspective neural networks (INN) method by enhancing INN's ...
Fan, Fan, Lee, Kwonjoon, Tu, Zhuowen, Xu, Weijian   +3 more
core   +1 more source

Generative Adversarial Optical Networks Using Diffractive Layers for Digit and Action Generation

Photonics
Within the traditional electronic neural network framework, Generative Adversarial Networks (GANs) have achieved extensive applications across multiple domains, including image synthesis, style transfer and data augmentation.
Pei Hu, Tengyu Cui, Yuanyuan Zhang, Shuai Feng   +3 more
doaj   +1 more source

Unrolled Generative Adversarial Networks

CoRR, 2016
We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice, and using the current value of ...
Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein   +3 more
openaire   +3 more sources

A progressive growing of conditional generative adversarial networks model

Dianxin kexue, 2023
Progressive growing of generative adversarial networks (PGGAN) is an adversarial network model that can generate high-resolution images.However, when the categories of samples are unbalanced, or the categories of samples are too similar or too dissimilar,
Hui MA, Ruiqin WANG, Shuai YANG
doaj   +2 more sources

A generative adversarial network to Reinhard stain normalization for histopathology image analysis

Ain Shams Engineering Journal
Histopathology image analysis is paramount importance for accurate diagnosing diseases and gaining insight into tissue properties. The significant challenge of staining variability continues.
Afnan M. Alhassan
doaj   +1 more source