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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Journal ArticleDOI
Robust detection for network intrusion of industrial IoT based on multi-CNN fusion
Yanmiao Li,Yanmiao Li,Yingying Xu,Zhi Liu,Haixia Hou,Yushuo Zheng,Yang Xin,Yuefeng Zhao,Lizhen Cui +8 more
TL;DR: The experimental results successfully demonstrate that the multi-CNN fusion model is very suitable for providing a classification method with high accuracy and low complexity on the NSL-KDD dataset and its performance is also superior to those of traditional machine learning methods and other recent deep learning approaches for binary classification and multiclass classification.
Journal ArticleDOI
Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR)
TL;DR: In this article, a machine learning algorithm (MLA) and a deep learning algorithm(DLA) were used to develop groundwater potential maps using support vector regression (SVR) and convolution neural network (CNN) functions, respectively.
Journal ArticleDOI
Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures
TL;DR: The testing results indicate that the proposed approach outperforms most of existing state-of-the-art methods, such as histogram of oriented 4-D normals and Actionlet on MSRAction3D.
Journal ArticleDOI
Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey
TL;DR: This survey is going to take a glance at the evolution of both semantic and instance segmentation work based on CNN, and specified comparative architectural details of some state-of-the-art models.
Journal ArticleDOI
A systematic review of convolutional neural network-based structural condition assessment techniques
TL;DR: A detailed literature review of existing CNN-based techniques in the context of infrastructure monitoring and maintenance and a brief conclusion on potential future research directions of CNN in structural condition assessment is presented.
References
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Diederik P. Kingma,Jimmy Ba +1 more
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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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
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.