Results 31 to 40 of about 62,654 (265)
Selective kernel networks for weakly supervised relation extraction
CAAI Transactions on Intelligence Technology, 2021 The purpose of relation extraction is to identify the semantic relations between entities in sentences that contain two entities. Recently, many variants of the convolution neural network (CNN) have been introduced to relation extraction for the ...
Ziyang Li +4 more
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BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK
Iraqi Journal of Information & Communication Technology, 2021 Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image.
Meryem H. Muhson, Ayad A. Al-Ani
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Polar codes decoding algorithm based on convolutional neural network
Dianxin kexue, 2020 In order to solve the problem that the existing Polar code decoding algorithm based on neural network can only decode short codewords (codewords length N≤64),a new decoding algorithm using convolution neural network for long codewords (N≥512) was put ...
Rui GUO, Fanchun RAN
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Aerial Image Semantic Classification Method Based on Improved Full Convolution Neural Network [PDF]
Jisuanji gongcheng, 2017 The existing Convolution Neural Networks(CNNs) method cannot semantically identify each pixel,and it is difficult to decompose the different types of images from the pixel level.Therefore,an end-to-end full-convolution depth network is proposed to ...
YI Meng,SUI Lichun
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Image deblurring method driven by double layer convolution neural network denoising module
Nantong Daxue xuebao. Ziran kexue ban, 2022 To solve this problem for inflexible of noise levels for deep convolution neural network for image denoising, an image deblurring method driven by a double deep convolution neural network for image denoising is proposed.The learning capability of ...
WU Jingjing; MA Jingning; ZHU Yonggui
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Convolutional Neural Networks In Convolution
CoRR, 2018 Currently, increasingly deeper neural networks have been applied to improve their accuracy. In contrast, We propose a novel wider Convolutional Neural Networks (CNN) architecture, motivated by the Multi-column Deep Neural Networks and the Network In Network(NIN), aiming for higher accuracy without input data transmutation.
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Quantum convolutional neural networks [PDF]
Nature Physics, 2019 12 pages, 11 figures. v2: New application to optimizing quantum error correction codes, added sample complexity analysis, more details for experimental realizations, and other minor ...
Iris Cong +2 more
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Dysarthria detection using convolution neural network
Measurement: Sensors, 2023 Dysarthria patients have difficulty controlling their speaking muscles, resulting in incomprehensible speech. A number of studies have looked into speech impairments; however, more research is needed to consider speakers with the same impairment but ...
M. Mahendran, R. Visalakshi, S. Balaji
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An Introduction to Convolutional Neural Networks
CoRR, 2015 10 pages, 5 ...
Keiron O'Shea, Ryan Nash
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Detection algorithm of electronic disguised voice based on convolutional neural network
Dianxin kexue, 2018 An electronic disguised voice detection algorithm based on the statistical features of MFCC and the convolution neural network was proposed.Firstly,the statistical features of MFCC were extracted and reconstructed as the input of convolution neural ...
Hongwei XU +5 more
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