Recent advances in convolutional neural networks
Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +10 more
TLDR
A broad survey of the recent advances in convolutional neural networks can be found in this article, where the authors discuss the improvements of CNN on different aspects, namely, layer design, activation function, loss function, regularization, optimization and fast computation.About:
This article is published in Pattern Recognition.The article was published on 2018-05-01 and is currently open access. It has received 3125 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Applied imagery pattern recognition for photovoltaic modules’ inspection: A review on methods, challenges and future development
TL;DR: In this paper , the authors present a literature review of applied imagery pattern recognition (AIPR) for the inspection of photovoltaic (PV) modules under the main used spectra: (1) true-color RGB, (2) long-wave infrared (LWIR), and (3) electroluminescence-based short wave infrared (SWIR).
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Variable weight algorithm for convolutional neural networks and its applications to classification of seizure phases and types
TL;DR: The variable weight convolutional neural networks (VWCNNs) as discussed by the authors are a type of network structure employing dynamic weights instead of static weights in their convolution layers and fully-connected layers, which can be viewed as an infinite number of traditional, fixed-weight CNNs.
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Review: Application of Convolutional Neural Network in Defect Detection of 3C Products
TL;DR: Based on the development of CNN, the authors summarizes the defect detection method of 3C products by CNN with different depths, and analyzes the opportunities and challenges of different CNN frameworks, and exhibit the strategies for different application scenarios.
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SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising
TL;DR: A deep learning based approach to denoising images and restoring features using stack Denoising convolutional autoencoder to restore the structural details of fundus as well as to decrease the noise level.
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Two dimensional correlation spectroscopy combined with ResNet: Efficient method to identify bolete species compared to traditional machine learning
TL;DR: In this paper , different data pretreatment was carried out for the Fourier transform near infrared (FT-NIR) spectra, and the modeling results of partial least squares discrimination analysis (PLS-DA), support vector machines (SVM) and residual neural network (ResNet) were compared.
References
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
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