Results 41 to 50 of about 26,076 (260)

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

Graphical Generative Adversarial Networks

CoRR, 2018
We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data. Graphical-GAN conjoins the power of Bayesian networks on compactly representing the dependency structures among random variables and that of generative adversarial networks on learning expressive dependency functions.
Chongxuan Li, Max Welling, Jun Zhu 0001, Bo Zhang 0010   +3 more
openaire   +3 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

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

Coupled Generative Adversarial Networks

CoRR, 2016
We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast to the existing approaches, which require tuples of corresponding images in different domains in the training set, CoGAN can learn a joint distribution without any tuple of corresponding images.
Ming-Yu Liu 0001, Oncel Tuzel
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

Functionalizing Micro‐to‐Mesoscopic Electrode Architectures for Regulating Electron Transfer Behaviors in Electrocatalysis

Advanced Functional Materials, EarlyView.
A systematic review is conducted to assess the influence of electrode architecture across micro‐ to mesoscopic length scales on electron‐transfer pathways in electrocatalysis. We discuss the structure‐activity relationships in electrocatalytic applications, including resource recovery and environmental remediation, and provide cost‐effective, efficient
Manshu Zhao, Jiaojiao Zhang, Shideng Yuan, Xinmeng Yu, Keming Wan, Xiaoguang Duan, Zhining Wang   +6 more
wiley   +1 more source

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
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

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source