SEGAN: Speech Enhancement Generative Adversarial Network [PDF]
Interspeech, 2017 Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics.
Bonafonte, Antonio +2 more
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A generative adversarial network with multiscale and attention mechanisms for underwater image enhancement [PDF]
Scientific ReportsUnderwater images collected are often of low clarity and suffer from severe color distortion due to the marine environment and Illumination conditions.
Liquan Zhao, Yuda Li, Tie Zhong
doaj +2 more sources
A Tunnel Crack Segmentation and Recognition Algorithm Using SPGD-and-Generative Adversarial Network Fusion [PDF]
SensorsIn order to improve the recognition ability of tunnel cracks in the UAV platform with a vision imaging system in the UAV platform with a vision imaging system, this paper proposes a tunnel crack segmentation algorithm using SPGD-and-generative ...
Wei Sun, Xiaohu Liu, Zhiyong Lei
doaj +2 more sources
Design of e-commerce product price prediction model based on generative adversarial network with adaptive weight adjustment [PDF]
Scientific ReportsE-commerce platforms have amassed extensive transaction data, which serves as a valuable source for price prediction. However, the diversity of commodities poses challenges such as data imbalance, model overfitting, and underfitting.
Abuduaini Abudureheman +2 more
doaj +2 more sources
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
doaj +1 more source
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
doaj +1 more source
Depth-Aware Generative Adversarial Network for Talking Head Video Generation [PDF]
Computer Vision and Pattern Recognition, 2022 Talking head video generation aims to produce a synthetic human face video that contains the identity and pose information respectively from a given source image and a driving video.
Fa-Ting Hong +3 more
semanticscholar +1 more source
TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network [PDF]
IEEE Transactions on Image Processing, 2022 The end-to-end image fusion framework has achieved promising performance, with dedicated convolutional networks aggregating the multi-modal local appearance.
Dongyu Rao, Xiaojun Wu, Tianyang Xu
semanticscholar +1 more source
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
doaj +1 more source
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [PDF]
Computer Vision and Pattern Recognition, 2016 Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at ...
C. Ledig +8 more
semanticscholar +1 more source