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
A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition
TL;DR: This work presents a fall detection and activity recognition system that not only considers various activities of daily living but also considers detection of falls while taking into consideration the direction and severity.
Journal ArticleDOI
Quantifying defects in thin films using machine vision
Nina Taherimakhsousi,Benjamin P. MacLeod,Fraser G. L. Parlane,Thomas D. Morrissey,Edward P. Booker,Kevan E. Dettelbach,Curtis P. Berlinguette +6 more
TL;DR: In this article, a CNN is used for thin-film image analysis, which can identify and quantify the extent of a variety of defects and is applicable to multiple materials and imaging conditions.
Journal ArticleDOI
Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis.
Michael Platten,Michael Platten,Michael Platten,Irene Brusini,Irene Brusini,Olle Andersson,Russell Ouellette,Russell Ouellette,Fredrik Piehl,Fredrik Piehl,Chunliang Wang,Tobias Granberg,Tobias Granberg +12 more
TL;DR: DeepnCCA as mentioned in this paper is a supervised machine learning algorithm for corpus callosum segmentation and relate callosal morphology to clinical disability using conventional MRI scans collected in clinical routine.
Journal ArticleDOI
A Synchronous Prediction Model Based on Multi-Channel CNN with Moving Window for Coal and Electricity Consumption in Cement Calcination Process
TL;DR: In this paper, a data driven forecast approach combining moving window and multi-channel convolutional neural networks (MWMC-CNN) was proposed to predict electricity and coal consumption synchronously, in which the moving window was designed to extract the time-varying delay feature of the time series data to overcome its impact on energy consumption prediction, and the multichannel structure is designed to reduce the impact of the redundant parameters between weakly correlated variables of energy prediction.
Book ChapterDOI
Classification of Plants Using Convolutional Neural Network
TL;DR: A deep learning technique for plant leaf classification with the help of deep Convolutional Neural Network as a substitute of conventional classification methods like k-nearest neighbor, probabilistic neural network, support vector machine, genetic algorithm, and principal component analysis, which all need feature extraction and are time-consuming.
References
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Gradient-based learning applied to document recognition
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