What is fractionally-strided convolution layer?
$begingroup$
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 3 mins ago
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$begingroup$
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 3 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 3 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 3 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 3 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 3 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 3 mins ago
Haha TTproHaha TTpro
1012
asked 3 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.
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