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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A hybrid deep learning based intrusion detection system using spatial-temporal representation of in-vehicle network traffic
TL;DR: In this article , a hybrid deep learning-based intrusion detection system (HyDL-IDS) based upon spatial-temporal representation for characterizing in-vehicle network traffic accurately was proposed.
Journal ArticleDOI
Real-time recognition of arc weld pool using image segmentation network
TL;DR: In this article, the U-Net architecture was used to detect the weld pool boundary under various welding conditions, such as welding current, welding speed, and weld pool shape, which can be trained end-to-end from few images to perform well.
Journal ArticleDOI
Multi-Input Dual-Stream Capsule Network for Improved Lung and Colon Cancer Classification
Mumtaz Ali,Mumtaz Ali,Riaz Ali +2 more
TL;DR: In this paper, a multi-input capsule network and digital histopathology images were used to build an enhanced computerized diagnosis system for detecting squamous cell carcinomas and adenocarcinomas of the lungs, as well as adenocalarcinoma of the colon.
Proceedings ArticleDOI
An Ensemble of Convolutional Neural Networks for Image Classification Based on LSTM
TL;DR: An ensemble method using LSTM to obtain image features that represent the image more comprehen-sive and the accuracy of classification using ensemble features is significantly higher than that of a single model.
Journal ArticleDOI
Convolutional neural network ensemble for Parkinson's disease detection from voice recordings.
Máté Hireš,Matej Gazda,Peter Drotar,Nemuel Daniel Pah,Nemuel Daniel Pah,Mohammod Abdul Motin,Dinesh Kumar +6 more
TL;DR: In this paper, an ensemble of convolutional neural networks (CNNs) was used for the detection of Parkinson's disease from the voice recordings of 50 healthy people and 50 people with PD obtained from PC-GITA, a publicly available database.
References
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Proceedings ArticleDOI
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.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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.