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Accelerating the Super-Resolution Convolutional Neural Network
European Conference on Computer Vision, 2016As a successful deep model applied in image super-resolution (SR), the Super-Resolution Convolutional Neural Network (SRCNN) [1, 2] has demonstrated superior performance to the previous hand-crafted models either in speed and restoration quality. However,
Chao Dong, Chen Change Loy, Xiaoou Tang
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2021
In this chapter we introduce the convolutional neural network theory including concepts such as convolution operator, kernel, stride, padding and pooling.
Ullo S. L. +7 more
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In this chapter we introduce the convolutional neural network theory including concepts such as convolution operator, kernel, stride, padding and pooling.
Ullo S. L. +7 more
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2018
The previously discussed architecture of ANNs is called FC neural networks (FCNNs). The reason is that each neuron in a layer i is connected to all neurons in layers i-1 and i+1. Each connection between two neurons has two parameters: the weight and the bias. Adding more layers and neurons increases the number of parameters.
Mathew Salvaris +2 more
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The previously discussed architecture of ANNs is called FC neural networks (FCNNs). The reason is that each neuron in a layer i is connected to all neurons in layers i-1 and i+1. Each connection between two neurons has two parameters: the weight and the bias. Adding more layers and neurons increases the number of parameters.
Mathew Salvaris +2 more
+4 more sources
Programming with TensorFlow, 2020
Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can ...
Ravishankar Chityala, Sridevi Pudipeddi
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Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can ...
Ravishankar Chityala, Sridevi Pudipeddi
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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
European Conference on Computer Vision, 2016We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32\(\times \) memory saving.
Mohammad Rastegari +3 more
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ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars
International Symposium on Computer Architecture, 2016A number of recent efforts have attempted to design accelerators for popular machine learning algorithms, such as those involving convolutional and deep neural networks (CNNs and DNNs).
Ali Shafiee +7 more
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A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method
IEEE transactions on industrial electronics (1982. Print), 2018Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional
Long Wen +3 more
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IEEE Transactions on Image Processing, 2022
Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), such as Graph Attention Networks (GAT), are two classic neural network models, which are applied to the processing of grid data and graph data respectively.
Yanni Dong, Quanwei Liu, Bo Du, L. Zhang
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Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), such as Graph Attention Networks (GAT), are two classic neural network models, which are applied to the processing of grid data and graph data respectively.
Yanni Dong, Quanwei Liu, Bo Du, L. Zhang
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Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition
IEEE International Conference on Computer Vision, 2017Recognizing fine-grained categories (e.g., bird species) highly relies on discriminative part localization and part-based fine-grained feature learning.
Heliang Zheng +3 more
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2019
In the last few years, convolutional neural networks (CNNs), along with recurrent neural networks (RNNs), have become a basic building block in constructing complex deep learning solutions for various NLP, speech, and time series tasks. LeCun first introduced certain basic parts of the CNN frameworks as a general NN framework to solve various high ...
Uday Kamath, John Liu, James Whitaker
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In the last few years, convolutional neural networks (CNNs), along with recurrent neural networks (RNNs), have become a basic building block in constructing complex deep learning solutions for various NLP, speech, and time series tasks. LeCun first introduced certain basic parts of the CNN frameworks as a general NN framework to solve various high ...
Uday Kamath, John Liu, James Whitaker
openaire +1 more source