Results 11 to 20 of about 43,961 (294)
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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Generating mobility networks with generative adversarial networks
EPJ Data Science, 2022 AbstractThe increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city’s entire mobility network, a weighted ...
Mauro, Giovanni +4 more
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Seismic random noise suppression using improved CycleGAN
Frontiers in Earth Science, 2023 Random noise adversely affects the signal-to-noise ratio of complex seismic signals in complex surface conditions and media. The primary challenges related to processing seismic data have always been reducing the random noise and increasing the signal-to-
Shimin Sun +8 more
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Generative Adversarial Networks
International Journal for Research in Applied Science and Engineering Technology, 2021 Abstract: Deep learning's breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results.
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Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN
Complexity, 2021 Aiming at the problem of radar target recognition of High-Resolution Range Profile (HRRP) under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network (CN-LSGAN), Short-time ...
Jianghua Nie +3 more
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Mechanical and electrical equipment fault diagnosis based on dual attention mechanism and S-BiGAN
Hangkong gongcheng jinzhan, 2023 The accurate fault diagnosis of mechanical and electrical equipment under the condition of limited label samples is of great significance for improving the health management ability of complex mechanical and electrical equipment.
JIAO Xiaoxuan +4 more
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Spatial evolutionary generative adversarial networks [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference, 2019 Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse. These pathologies mainly arise from a lack of diversity in their adversarial interactions. Evolutionary generative adversarial networks apply the principles of evolutionary computation to mitigate these problems.
Toutouh, Jamal +2 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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HGAN: Hybrid generative adversarial network [PDF]
Journal of Intelligent & Fuzzy Systems, 2021 In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood.
Iranmanesh, Seyed Mehdi +1 more
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IEEE Access, 2020 With the appearance of Generative Adversarial Network (GAN), image-to-image translation based on a new unified framework has attracted growing interests.
Guifang Shao +4 more
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