What is fractionally-strided convolution layer? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsWhat are deconvolutional layers?What are deconvolutional layers?How do subsequent convolution layers work?How are 1x1 convolutions the same as a fully connected layer?Do all layers have the same computational complexity in a ResNet?Depth of the first pooling layer outcome in tensorflow documentationWhat principle is behind semantic segmenation with CNNs?Understand the shape of this Convolutional Neural NetworkIs color information only extracted in the first input layer of a convolutional neural network?Subsequent convolution layersWhat is the motivation for row-wise convolution and folding in Kalchbrenner et al. (2014)?
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What is fractionally-strided convolution layer?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsWhat are deconvolutional layers?What are deconvolutional layers?How do subsequent convolution layers work?How are 1x1 convolutions the same as a fully connected layer?Do all layers have the same computational complexity in a ResNet?Depth of the first pooling layer outcome in tensorflow documentationWhat principle is behind semantic segmenation with CNNs?Understand the shape of this Convolutional Neural NetworkIs color information only extracted in the first input layer of a convolutional neural network?Subsequent convolution layersWhat is the motivation for row-wise convolution and folding in Kalchbrenner et al. (2014)?
$begingroup$
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 convnet computer-vision convolution
edited Apr 15 at 6:33
Esmailian
3,431420
asked Apr 15 at 3:26
Haha TTproHaha TTpro
1134
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 convnet computer-vision convolution
edited Apr 15 at 6:33
Esmailian
3,431420
asked Apr 15 at 3:26
Haha TTproHaha TTpro
1134
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 convnet computer-vision convolution
edited Apr 15 at 6:33
Esmailian
3,431420
asked Apr 15 at 3:26
Haha TTproHaha TTpro
1134
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 convnet computer-vision convolution
deep-learning convnet computer-vision convolution
edited Apr 15 at 6:33
Esmailian
3,431420
asked Apr 15 at 3:26
Haha TTproHaha TTpro
1134
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.
edited Apr 15 at 6:33
Esmailian
3,431420
asked Apr 15 at 3:26
Haha TTproHaha TTpro
1134
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.
edited Apr 15 at 6:33
Esmailian
3,431420
edited Apr 15 at 6:33
Esmailian
3,431420
edited Apr 15 at 6:33
Esmailian
3,431420
3,431420
asked Apr 15 at 3:26
Haha TTproHaha TTpro
1134
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 Apr 15 at 3:26
Haha TTproHaha TTpro
1134
asked Apr 15 at 3:26
Haha TTproHaha TTpro
1134
1134
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 |
1 Answer
1
active
oldest
votes
$begingroup$
Here is an animation of fractionally-strided convolution (from this github project):
where the dashed white cells are zero rows/columns padded between the input cells (blue). These animations are visualizations of the mathematical formulas from the article below:
A guide to convolution arithmetic for deep learning
Here is a quote from the article:
Figure [..] helps understand what fractional strides involve: zeros
are inserted between input units, which makes the kernel move around
at a slower pace than with unit strides [footnote: doing so is
inefficient and real-world implementations avoid useless
multiplications by zero, but conceptually it is how the transpose of a
strided convolution can be thought of.]
Also, here is a post on this site asking "What are deconvolutional layers?" which is the same thing.
And here are two quotes from a post by Paul-Louis Pröve on different types of convolutions:
Transposed Convolutions (a.k.a. deconvolutions or fractionally strided
convolutions)
and
Some sources use the name deconvolution, which is inappropriate
because it’s not a deconvolution [..] An actual deconvolution reverts the process of a convolution.
answered Apr 15 at 6:08
EsmailianEsmailian
3,431420
$endgroup$
add a comment |
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1 Answer
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active
oldest
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1 Answer
1
active
oldest
votes
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oldest
votes
$begingroup$
Here is an animation of fractionally-strided convolution (from this github project):
where the dashed white cells are zero rows/columns padded between the input cells (blue). These animations are visualizations of the mathematical formulas from the article below:
A guide to convolution arithmetic for deep learning
Here is a quote from the article:
Figure [..] helps understand what fractional strides involve: zeros
are inserted between input units, which makes the kernel move around
at a slower pace than with unit strides [footnote: doing so is
inefficient and real-world implementations avoid useless
multiplications by zero, but conceptually it is how the transpose of a
strided convolution can be thought of.]
Also, here is a post on this site asking "What are deconvolutional layers?" which is the same thing.
And here are two quotes from a post by Paul-Louis Pröve on different types of convolutions:
Transposed Convolutions (a.k.a. deconvolutions or fractionally strided
convolutions)
and
Some sources use the name deconvolution, which is inappropriate
because it’s not a deconvolution [..] An actual deconvolution reverts the process of a convolution.
answered Apr 15 at 6:08
EsmailianEsmailian
3,431420
$endgroup$
add a comment |
$begingroup$
Here is an animation of fractionally-strided convolution (from this github project):
where the dashed white cells are zero rows/columns padded between the input cells (blue). These animations are visualizations of the mathematical formulas from the article below:
A guide to convolution arithmetic for deep learning
Here is a quote from the article:
Figure [..] helps understand what fractional strides involve: zeros
are inserted between input units, which makes the kernel move around
at a slower pace than with unit strides [footnote: doing so is
inefficient and real-world implementations avoid useless
multiplications by zero, but conceptually it is how the transpose of a
strided convolution can be thought of.]
Also, here is a post on this site asking "What are deconvolutional layers?" which is the same thing.
And here are two quotes from a post by Paul-Louis Pröve on different types of convolutions:
Transposed Convolutions (a.k.a. deconvolutions or fractionally strided
convolutions)
and
Some sources use the name deconvolution, which is inappropriate
because it’s not a deconvolution [..] An actual deconvolution reverts the process of a convolution.
answered Apr 15 at 6:08
EsmailianEsmailian
3,431420
$endgroup$
add a comment |
$begingroup$
Here is an animation of fractionally-strided convolution (from this github project):
where the dashed white cells are zero rows/columns padded between the input cells (blue). These animations are visualizations of the mathematical formulas from the article below:
A guide to convolution arithmetic for deep learning
Here is a quote from the article:
Figure [..] helps understand what fractional strides involve: zeros
are inserted between input units, which makes the kernel move around
at a slower pace than with unit strides [footnote: doing so is
inefficient and real-world implementations avoid useless
multiplications by zero, but conceptually it is how the transpose of a
strided convolution can be thought of.]
Also, here is a post on this site asking "What are deconvolutional layers?" which is the same thing.
And here are two quotes from a post by Paul-Louis Pröve on different types of convolutions:
Transposed Convolutions (a.k.a. deconvolutions or fractionally strided
convolutions)
and
Some sources use the name deconvolution, which is inappropriate
because it’s not a deconvolution [..] An actual deconvolution reverts the process of a convolution.
answered Apr 15 at 6:08
EsmailianEsmailian
3,431420
$endgroup$
Here is an animation of fractionally-strided convolution (from this github project):
where the dashed white cells are zero rows/columns padded between the input cells (blue). These animations are visualizations of the mathematical formulas from the article below:
A guide to convolution arithmetic for deep learning
Here is a quote from the article:
Figure [..] helps understand what fractional strides involve: zeros
are inserted between input units, which makes the kernel move around
at a slower pace than with unit strides [footnote: doing so is
inefficient and real-world implementations avoid useless
multiplications by zero, but conceptually it is how the transpose of a
strided convolution can be thought of.]
Also, here is a post on this site asking "What are deconvolutional layers?" which is the same thing.
And here are two quotes from a post by Paul-Louis Pröve on different types of convolutions:
Transposed Convolutions (a.k.a. deconvolutions or fractionally strided
convolutions)
and
Some sources use the name deconvolution, which is inappropriate
because it’s not a deconvolution [..] An actual deconvolution reverts the process of a convolution.
answered Apr 15 at 6:08
EsmailianEsmailian
3,431420
edited Apr 15 at 9:04
answered Apr 15 at 6:08
EsmailianEsmailian
3,431420
answered Apr 15 at 6:08
EsmailianEsmailian
3,431420
answered Apr 15 at 6:08
EsmailianEsmailian
3,431420
3,431420
add a comment |
add a comment |
Haha TTpro is a new contributor. Be nice, and check out our Code of Conduct.
Haha TTpro is a new contributor. Be nice, and check out our Code of Conduct.
Haha TTpro is a new contributor. Be nice, and check out our Code of Conduct.
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