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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… Expand
Wikipedia

Related topics

Related topics

49 relations
Analog signal processingBlob detectionCausal filterChasys 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
Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The… Expand
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Highly Cited
2017
Highly Cited
2017
Rethinking Atrous Convolution for Semantic Image Segmentation
In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the… Expand
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Highly Cited
2017
Highly Cited
2017
Semi-Supervised Classification with Graph Convolutional Networks
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of… Expand
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Highly Cited
2017
Highly Cited
2017
ECO: Efficient Convolution Operators for Tracking
In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in… Expand
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Highly Cited
2017
Highly Cited
2017
Xception: Deep Learning with Depthwise Separable Convolutions
  • François Chollet
  • IEEE Conference on Computer Vision and Pattern…
  • 2017
    • Corpus ID: 2375110
We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between… Expand
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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… Expand
Highly Cited
2015
Highly Cited
2015
Very Deep Convolutional Networks for Large-Scale Image Recognition
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition… Expand
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… Expand
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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… Expand
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Review
1999
Review
1999
Convolution kernels on discrete structures
We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs… Expand
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