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Convolution
Known as:
Convolve
, Carson's integral
, Continuous-time convolution
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In mathematics (and, in particular, functional analysis) convolution is a mathematical operation on two functions (f and g); it produces a third…
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Related topics
Related topics
49 relations
Analog signal processing
Blob detection
Causal filter
Chasys Draw IES
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Broader (1)
Image processing
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2018
Highly Cited
2018
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Liang-Chieh Chen
,
Yukun Zhu
,
G. Papandreou
,
Florian Schroff
,
Hartwig Adam
European Conference on Computer Vision
2018
Corpus ID: 3638670
Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The…
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Highly Cited
2017
Highly Cited
2017
Rethinking Atrous Convolution for Semantic Image Segmentation
Liang-Chieh Chen
,
G. Papandreou
,
Florian Schroff
,
Hartwig Adam
arXiv.org
2017
Corpus ID: 22655199
In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the…
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Highly Cited
2017
Highly Cited
2017
Understanding Convolution for Semantic Segmentation
Panqu Wang
,
Pengfei Chen
,
+4 authors
G. Cottrell
IEEE Workshop/Winter Conference on Applications…
2017
Corpus ID: 4599765
Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over…
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Highly Cited
2016
Highly Cited
2016
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf
,
M. Welling
International Conference on Learning…
2016
Corpus ID: 3144218
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of…
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Highly Cited
2016
Highly Cited
2016
Xception: Deep Learning with Depthwise Separable Convolutions
François Chollet
Computer Vision and Pattern Recognition
2016
Corpus ID: 2375110
We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between…
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Highly Cited
2014
Highly Cited
2014
Going deeper with convolutions
Christian Szegedy
,
Wei Liu
,
+6 authors
Andrew Rabinovich
Computer Vision and Pattern Recognition
2014
Corpus ID: 206592484
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for…
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Highly Cited
2014
Highly Cited
2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan
,
Andrew Zisserman
International Conference on Learning…
2014
Corpus ID: 14124313
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition…
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Highly Cited
2012
Highly Cited
2012
ImageNet classification with deep convolutional neural networks
A. Krizhevsky
,
I. Sutskever
,
Geoffrey E. Hinton
Communications of the ACM
2012
Corpus ID: 195908774
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC…
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Highly Cited
2001
Highly Cited
2001
Convolution Kernels for Natural Language
M. Collins
,
Nigel P. Duffy
Neural Information Processing Systems
2001
Corpus ID: 396794
We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being…
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Review
1999
Review
1999
Convolution kernels on discrete structures
D. Haussler
1999
Corpus ID: 17702358
We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs…
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