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Analysis and prediction of schizophrenia patients based on high-order graph attention generative adversarial networks [PDF]
Scientific ReportsGenerative Adversarial Networks, a popular deep learning method, have achieved excellent performance in both classification and prediction tasks. However, there have been relatively few applications of generative adversarial networks to EEG data.
Guimei Yin +11 more
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Exploring generative adversarial networks and adversarial training
International Journal of Cognitive Computing in Engineering, 2022 Recognized as a realistic image generator, Generative Adversarial Network (GAN) occupies a progressive section in deep learning. Using generative modeling, the underlying generator model learns the real target distribution and outputs fake samples from ...
Afia Sajeeda, B M Mainul Hossain, Ph.D
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Conditional noise generative adversarial networks with Siamese neural network for longer time series forecasting [PDF]
Scientific ReportsGenerative adversarial networks have achieved strong results in computer vision, but their use in time series forecasting remains limited. This paper proposes a conditional noise generative adversarial network with a Siamese neural network as ...
Haotian Mao, Xiao Feng
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Super-resolution Reconstruction of MRI Based on DNGAN [PDF]
Jisuanji kexue, 2022 The quality of MRI will affect doctor's judgment on patient's physical conditions,and the high-resolution MRI is more conducive to doctor to make an accurate diagnosis.Using computer technology to perform super-resolution reconstruction of MRI can obtain
DAI Zhao-xia, LI Jin-xin, ZHANG Xiang-dong, XU Xu, MEI Lin, ZHANG Liang
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IEEE Access, 2022 An adversarial example, which is an input instance with small, intentional feature perturbations to machine learning models, represents a concrete problem in Artificial intelligence safety.
Seok-Hwan Choi +3 more
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A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images
Remote Sensing, 2023 High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution ...
Xuan Wang +3 more
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Multi-Stage Hybrid Text-to-Image Generation Models [PDF]
International Journal of Intelligent Computing and Information Sciences, 2022 Generative Adversarial Networks (GANs) have proven their outstanding potential in creating realistic images that can't differentiate between them and the real images, but text-to-image (conditional generation) still faces some challenges.
Razan Bayoumi +2 more
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Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation
Applied Sciences, 2021 A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from standard models are commonly used to build computer recognition algorithms for acquiring more knowledge on various and low-cost research issues. Convolutional
Christine Dewi +3 more
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PEGANs: Phased Evolutionary Generative Adversarial Networks with Self-Attention Module
Mathematics, 2022 Generative adversarial networks have made remarkable achievements in generative tasks. However, instability and mode collapse are still frequent problems.
Yu Xue +3 more
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BoostNet: A Boosted Convolutional Neural Network for Image Blind Denoising
IEEE Access, 2021 Deep convolutional neural networks and generative adversarial networks currently attracted the attention of researchers because it is more effective than conventional representation-based methods.
Duc My Vo +3 more
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