Understanding of a convolutional neural network [PDF]
The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literature as it is able to handle a huge amount of data. The interest in having deeper hidden layers has recently begun to
Saad Albawi+2 more
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Convolutional neural networks are designed to work with grid-structured inputs, which have strong spatial dependencies in local regions of the grid. The most obvious example of grid-structured data is a 2-dimensional image. This type of data also exhibits spatial dependencies, because adjacent spatial locations in an image often have similar color ...
Mathew Salvaris+2 more
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Convolution-Bidirectional Temporal Convolutional Network for Protein Secondary Structure Prediction
As a basic feature extraction method, convolutional neural networks have some information loss problems when dealing with sequence problems, and a temporal convolutional network can compensate for this problem.
Yunqing Zhang, Yuming Ma, Yihui Liu
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Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network [PDF]
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled
Wenzhe Shi+7 more
semanticscholar +1 more source
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices [PDF]
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs).
Xiangyu Zhang+3 more
semanticscholar +1 more source
Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting [PDF]
Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect ...
Ting Yu, Haoteng Yin, Zhanxing Zhu
semanticscholar +1 more source
Pansharpening by Convolutional Neural Networks [PDF]
A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices ...
MASI, GIUSEPPE+3 more
openaire +4 more sources
Improved Convolutional Neural Image Recognition Algorithm based on LeNet-5
Convolutional neural network (CNN) is a very important method in deep learning, which solves many complex pattern recognition problems. Fruitful results have been achieved in image recognition, speech recognition, and natural language processing ...
Lijie Zhou, Weihai Yu
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Texture synthesis of ecological plant protection image based on convolution neural network
Texture synthesis technology is an important realistic rendering technology. Texture synthesis technology also has a good application prospect in image rendering and other fields. Convolutional neural network is a very popular technology in recent years.
Libing Hu, Fei Zhou, Xianjun Fu
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EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces [PDF]
Objective. Brain–computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI
Vernon J. Lawhern+5 more
semanticscholar +1 more source