Results 151 to 160 of about 222,396 (351)
Generative Adversarial Network in Medical Imaging: A Review [PDF]
Medical Image Anal., 2018 Xin Yi, Ekta Walia, P. Babyn
semanticscholar +1 more source
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Regional attention generative adversarial network
Electronics Letters, 2019 In this Letter, the authors propose a novel attention mechanism combined with a classical generative adversarial network (GAN) model to improve the visual quality of generated samples. This novel attention model is named regional attention GAN.
Wei Wang +4 more
doaj +1 more source
Jujube quality grading using a generative adversarial network with an imbalanced data set
, 2023 Hao Cang +7 more
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Advanced Intelligent Discovery, EarlyView.Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz +2 more
wiley +1 more source
Advances in generative adversarial network
Tongxin xuebao, 2018 Generative adversarial network (GAN) have swiftly become the focus of considerable research in generative models soon after its emergence,whose academic research and industry applications have yielded a stream of further progress along with the ...
Wanliang WANG, Zhuorong LI
doaj +2 more sources
Defending against and generating adversarial examples together with generative adversarial networks
Scientific ReportsAlthough deep neural networks have achieved great success in many tasks, they encounter security threats and are often fooled by adversarial examples, which are created by making slight modifications to pixel values. To address these problems, a novel DG-GAN framework is proposed, integrating generator, encoder, and discriminator, to defend against and
Ying Wang, Xiao Liao, Wei Cui, Yang Yang
openaire +3 more sources
Advanced Intelligent Discovery, EarlyView.A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali +5 more
wiley +1 more source
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
CamoGAN: Evolving optimum camouflage with Generative Adversarial Networks [PDF]
, 2019 László Tálas +5 more
openalex +1 more source