What is fractionally-strided convolution layer? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsHow can you decide the window size on a pooling layer?Depth of the first pooling layer outcome in tensorflow documentationWhy is this not ordinary convolution?Multi-image superresolution using CNNsConvolutional Neural Networks layer sizesIs color information only extracted in the first input layer of a convolutional neural network?Subsequent convolution layersLeNet-5 - combining feature maps in C3 layerWhat is the motivation for row-wise convolution and folding in Kalchbrenner et al. (2014)?How to choose the number of output channels in a convolutional layer?
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What is fractionally-strided convolution layer?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsHow can you decide the window size on a pooling layer?Depth of the first pooling layer outcome in tensorflow documentationWhy is this not ordinary convolution?Multi-image superresolution using CNNsConvolutional Neural Networks layer sizesIs color information only extracted in the first input layer of a convolutional neural network?Subsequent convolution layersLeNet-5 - combining feature maps in C3 layerWhat is the motivation for row-wise convolution and folding in Kalchbrenner et al. (2014)?How to choose the number of output channels in a convolutional layer?
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In paper Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs, in Section 3.4, it said
Since, the aim of this work is to estimate high-resolution and
high-quality density maps, F-CNN is constructed using a set of
convolutional and fractionally-strided convolutional layers. The set
of fractionally-strided convolutional layers help us to restore
details in the output density maps. The following structure is used
for F-CNN: CR(64,9)-CR(32,7)- TR(32)-CR(16,5)-TR(16)-C(1,1), where, C
is convolutional layer, R is ReLU layer, T is fractionally-strided
convolution layer and the first number inside every brace indicates
the number of filters while the second number indicates filter size.
Every fractionally-strided convolution layer increases the input
resolution by a factor of 2, thereby ensuring that the output
resolution is the same as that of input.
I would like to know the detail of fractionally-strided convolution layer.
deep-learning computer-vision convolution
asked 11 mins ago
Haha TTproHaha TTpro
1012
New contributor
Haha TTpro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
$begingroup$
In paper Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs, in Section 3.4, it said
Since, the aim of this work is to estimate high-resolution and
high-quality density maps, F-CNN is constructed using a set of
convolutional and fractionally-strided convolutional layers. The set
of fractionally-strided convolutional layers help us to restore
details in the output density maps. The following structure is used
for F-CNN: CR(64,9)-CR(32,7)- TR(32)-CR(16,5)-TR(16)-C(1,1), where, C
is convolutional layer, R is ReLU layer, T is fractionally-strided
convolution layer and the first number inside every brace indicates
the number of filters while the second number indicates filter size.
Every fractionally-strided convolution layer increases the input
resolution by a factor of 2, thereby ensuring that the output
resolution is the same as that of input.
I would like to know the detail of fractionally-strided convolution layer.
deep-learning computer-vision convolution
asked 11 mins ago
Haha TTproHaha TTpro
1012
New contributor
Haha TTpro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
In paper Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs, in Section 3.4, it said
Since, the aim of this work is to estimate high-resolution and
high-quality density maps, F-CNN is constructed using a set of
convolutional and fractionally-strided convolutional layers. The set
of fractionally-strided convolutional layers help us to restore
details in the output density maps. The following structure is used
for F-CNN: CR(64,9)-CR(32,7)- TR(32)-CR(16,5)-TR(16)-C(1,1), where, C
is convolutional layer, R is ReLU layer, T is fractionally-strided
convolution layer and the first number inside every brace indicates
the number of filters while the second number indicates filter size.
Every fractionally-strided convolution layer increases the input
resolution by a factor of 2, thereby ensuring that the output
resolution is the same as that of input.
I would like to know the detail of fractionally-strided convolution layer.
deep-learning computer-vision convolution
asked 11 mins ago
Haha TTproHaha TTpro
1012
New contributor
Haha TTpro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
In paper Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs, in Section 3.4, it said
Since, the aim of this work is to estimate high-resolution and
high-quality density maps, F-CNN is constructed using a set of
convolutional and fractionally-strided convolutional layers. The set
of fractionally-strided convolutional layers help us to restore
details in the output density maps. The following structure is used
for F-CNN: CR(64,9)-CR(32,7)- TR(32)-CR(16,5)-TR(16)-C(1,1), where, C
is convolutional layer, R is ReLU layer, T is fractionally-strided
convolution layer and the first number inside every brace indicates
the number of filters while the second number indicates filter size.
Every fractionally-strided convolution layer increases the input
resolution by a factor of 2, thereby ensuring that the output
resolution is the same as that of input.
I would like to know the detail of fractionally-strided convolution layer.
deep-learning computer-vision convolution
deep-learning computer-vision convolution
asked 11 mins ago
Haha TTproHaha TTpro
1012
New contributor
Haha TTpro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 mins ago
Haha TTproHaha TTpro
1012
New contributor
Haha TTpro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 mins ago
Haha TTproHaha TTpro
1012
New contributor
Haha TTpro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 mins ago
Haha TTproHaha TTpro
1012
asked 11 mins ago
Haha TTproHaha TTpro
1012
1012
New contributor
Haha TTpro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Haha TTpro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Haha TTpro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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