Results 301 to 310 of about 222,396 (351)

Improving conditional generative adversarial networks for inverse design of plasmonic structures

Persson Petter, Henriksson, Nils, Maccaferri, Nicolò   +2 more
openalex   +1 more source

Residual-based multivariate exponentially weighted moving average control chart for statistical process control of water quality in Surabaya city utilizing generative adversarial network. [PDF]

MethodsX
Ahsan M, Indarsanto RW, Rifki KAF, Lee MH.   +3 more
europepmc   +1 more source

Detection of cyber attacks in electric vehicle charging systems using a remaining useful life generative adversarial network. [PDF]

Sci Rep
Tanyıldız H, Şahin CB, Dinler ÖB, Migdady H, Saleem K, Smerat A, Gandomi AH, Abualigah L.   +7 more
europepmc   +1 more source

Image restoration for ring-array photoacoustic tomography based on an attention mechanism driven conditional generative adversarial network. [PDF]

Photoacoustics
Dong W, Zhang Y, Hu L, Liu S, Tian C.
europepmc   +1 more source

Single Fringe Phase Retrieval for Translucent Object Measurements Using a Deep Convolutional Generative Adversarial Network. [PDF]

Sensors (Basel)
He J, Huang Y, Wu J, Tang Y, Wang W.
europepmc   +1 more source

Traffic data imputation via knowledge graph-enhanced generative adversarial network. [PDF]

PeerJ Comput Sci
Liu Y, Shen G, Liu N, Han X, Xu Z, Zhou J, Kong X.   +6 more
europepmc   +1 more source

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FusionGAN: A generative adversarial network for infrared and visible image fusion

Information Fusion, 2019
Jiayi, Pengwei Liang, Chang Li
exaly   +2 more sources

GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion

IEEE Transactions on Instrumentation and Measurement, 2021
Visible images contain rich texture information, whereas infrared images have significant contrast. It is advantageous to combine these two kinds of information into a single image so that it not only has good contrast but also contains rich texture ...
Jiayi Ma, Hao Zhang, Z. Shao, Pengwei Liang, Han Xu   +4 more
semanticscholar   +1 more source

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   +3 more sources

DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion

IEEE Transactions on Image Processing, 2020
In this paper, we proposed a new end-to-end model, termed as dual-discriminator conditional generative adversarial network (DDcGAN), for fusing infrared and visible images of different resolutions.
Jiayi Ma, Han Xu, Junjun Jiang, Xiaoguang Mei, Xiao-Ping Zhang   +4 more
semanticscholar   +1 more source