Results 11 to 20 of about 60,338 (274)
Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation [PDF]
Computer Networks, 2021 Accepted for publication at Journal of Computer ...
Hojjat Navidan +6 more
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Poly-GAN: Regularizing Polygons with Generative Adversarial Networks [PDF]
, 2023 Regularizing polygons involves simplifying irregular and noisy shapes of built environment objects (e.g. buildings) to ensure that they are accurately represented using a minimum number of vertices. It is a vital processing step when creating/transmitting online digital maps so that they occupy minimal storage space and bandwidth. This paper presents a
Lasith Niroshan, James D. Carswell
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Applied Sciences, 2023 This paper introduces a novel and robust data-driven algorithm designed for Aircraft Trajectory Prediction (ATP). The approach employs a Neural Network architecture to predict future aircraft trajectories, utilizing input variables such as latitude ...
Seyed Mohammad Hashemi +3 more
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Generative Adversarial Network Based on Piecewise Loss [PDF]
Jisuanji gongcheng, 2019 Generative Adversarial Network(GAN) fails to effectively execute the synchronous update between generator and discriminator during training,resulting in unstable model training and mode collapse.To solve this problem,a generative adversarial network PL ...
LIU Qikai,JIANG Daihong,LI Wenji
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Validity Improvement in MolGAN-Based Molecular Generation
IEEE Access, 2023 Designing molecules that have desired properties is one of the challenging tasks of drug design. Among the many molecular generative models, a generative adversarial network (GAN), is able to generate molecule structures with desirable chemical ...
Jiayi Fan, Seul Ki Hong, Yongkeun Lee
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LDDMM Meets GANs: Generative Adversarial Networks for Diffeomorphic Registration
Jornada de Jóvenes Investigadores del I3A, 2021 In this work, we propose an unsupervised adversarial learning LDDMM method for 3D mono-modal images based on Generative Adversarial Networks. We have successfully implemented two models with stationary and EPDiff constrained non-stationary parameterizations of diffeomorphisms.
Ramón Júlvez, Ubaldo +2 more
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PL-GAN: Path Loss Prediction Using Generative Adversarial Networks
IEEE Access, 2022 Accurate prediction of path loss is essential for the design and optimization of wireless communication networks. Existing path loss prediction methods typically suffer from the trade-off between accuracy and computational efficiency. In this paper, we present a deep learning based approach with clear advantages over the existing ones.
Ahmed Marey +3 more
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Preset Conditional Generative Adversarial Network for Massive MIMO Detection
IET Signal Processing, 2023 In recent years, extensive research has been conducted to obtain better detection performance by combining massive multiple-input multiple-output (MIMO) signal detection with deep neural network (DNN).
Yongzhi Yu +3 more
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MB-GAN: Microbiome Simulation via Generative Adversarial Network [PDF]
GigaScience, 2019 AbstractSimulation is a critical component of experimental design and evaluation of analysis methods in microbiome association studies. However, statistically modeling the microbiome data is challenging since that the complex structure in the real data is difficult to be fully represented by statistical models.
Ruichen Rong +7 more
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Generative Adversarial Network for Medical Images (MI-GAN) [PDF]
Journal of Medical Systems, 2018 Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset, especially when one is dealing ...
Talha Iqbal, Hazrat Ali
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