Convolution

Known as: Convolve, Carson's integral, Continuous-time convolution 
In mathematics (and, in particular, functional analysis) convolution is a mathematical operation on two functions (f and g); it produces a third… (More)
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Image processing

Papers overview

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Highly Cited
2018
Highly Cited
2018
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are… (More)
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Highly Cited
2017
Highly Cited
2017
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a… (More)
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Highly Cited
2016
Highly Cited
2016
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In… (More)
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Highly Cited
2015
Highly Cited
2015
Learning Spatiotemporal Features with 3D Convolutional Networks
We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks… (More)
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Highly Cited
2015
Highly Cited
2015
Going deeper with convolutions
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for… (More)
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Highly Cited
2014
Highly Cited
2014
Striving for Simplicity: The All Convolutional Net
Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating… (More)
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Highly Cited
2012
Highly Cited
2012
ImageNet Classification with Deep Convolutional Neural Networks
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC… (More)
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Highly Cited
2003
Highly Cited
2003
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a… (More)
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Highly Cited
2001
Highly Cited
2001
Convolution Kernels for Natural Language
We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being… (More)
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Highly Cited
1981
Highly Cited
1981
Cubic Convolution Interpolation for Digital Image Processing
Absfrucf-Cubic convolution interpolation is a new technique for resampling discrete data. It has a number of desirable features… (More)
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