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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Proceedings ArticleDOI
Multi-Class Weather Classification Using ResNet-18 CNN for Autonomous IoT and CPS Applications
TL;DR: In this article, the authors proposed an accurate and precise self-reliant framework for weather recognition using ResNet-18 convolutional neural network to provide multi-class weather classification.
Journal ArticleDOI
A Deep Learning-Based Diagnosis System for COVID-19 Detection and Pneumonia Screening Using CT Imaging
Ramzi Mahmoudi,Narjes Benameur,Rania I Mabrouk,Mazin Abed Mohammed,Begonya Garcia-Zapirain,Mohamed Hedi Bedoui +5 more
TL;DR: In this paper , a U-net architecture based on CNN encoder and CNN decoder is introduced for a fast and precise image segmentation to obtain the lung and infection segmentation models.
Journal ArticleDOI
Programmable photonic neural networks combining WDM with coherent linear optics
TL;DR: In this paper , a neuron architecture that combines coherent optics with WDM is presented for a multifunctional programmable neural network platform, which accommodates four different operational modes over the same photonic hardware, supporting multi-layer, convolutional, fully-connected and power saving layers.
Journal ArticleDOI
Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
TL;DR: A fault-classification scheme for planetary gear sets by combining the advantages of the RP and a convolutional neural network (CNN) and recurrence plot analysis for the fault classification of planetary-gear sets is proposed.
Journal ArticleDOI
A Method Combining CNN and ELM for Feature Extraction and Classification of SAR Image
Peng Wang,Xiaomin Zhang,Yan Hao +2 more
TL;DR: The CNN-ELM algorithm proposed by combining the CNN and the ELM algorithm can realize the sparsity of the network, alleviate the overfitting problem, and speed up the convergence speed of thenetwork.
References
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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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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.