What's a difference between the neoperceptron and CNN?
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What's a difference (in terms of architecture) between the neoperceptron and CNN?
Both ANNs have hidden layers and scanners, as I understood, but many sources subdivide them in two classes.
neural-network cnn
edited yesterday
nbro
290417
asked Oct 25 '18 at 23:40
ШахШах
1257
$endgroup$
add a comment |
$begingroup$
What's a difference (in terms of architecture) between the neoperceptron and CNN?
Both ANNs have hidden layers and scanners, as I understood, but many sources subdivide them in two classes.
neural-network cnn
edited yesterday
nbro
290417
asked Oct 25 '18 at 23:40
ШахШах
1257
$endgroup$
add a comment |
$begingroup$
What's a difference (in terms of architecture) between the neoperceptron and CNN?
Both ANNs have hidden layers and scanners, as I understood, but many sources subdivide them in two classes.
neural-network cnn
edited yesterday
nbro
290417
asked Oct 25 '18 at 23:40
ШахШах
1257
$endgroup$
What's a difference (in terms of architecture) between the neoperceptron and CNN?
Both ANNs have hidden layers and scanners, as I understood, but many sources subdivide them in two classes.
neural-network cnn
neural-network cnn
edited yesterday
nbro
290417
asked Oct 25 '18 at 23:40
ШахШах
1257
edited yesterday
nbro
290417
asked Oct 25 '18 at 23:40
ШахШах
1257
edited yesterday
nbro
290417
edited yesterday
nbro
290417
edited yesterday
nbro
290417
290417
asked Oct 25 '18 at 23:40
ШахШах
1257
asked Oct 25 '18 at 23:40
ШахШах
1257
asked Oct 25 '18 at 23:40
ШахШах
1257
1257
add a comment |
add a comment |
1 Answer
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According to the research paper, neoperceptrons are a class of CNN that are not sensitive to rotations.
One of the issues with traditional kernels (that was the case before CNN and it is still true with them) is that the rotation of the input image would lead to different results, because the neurons in the dense layer would have different levels of activations.
With these new neurons, you don't get an issue with orientation. So in theory, if you have a gradient in your image, no matter what the orientation is, you would get the same value.
For a traditional CNN, you would get maximum activation with the original orientation, inverse with a 180° rotated image, and no activation with 90° or 270°.
answered Oct 26 '18 at 8:24
Matthieu BrucherMatthieu Brucher
69113
$endgroup$
$begingroup$
Thank you very much for explanation! Everything fell into place
$endgroup$
– Шах
Oct 26 '18 at 8:29
$begingroup$
Which research paper are you referring to?
$endgroup$
– nbro
yesterday
add a comment |
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1 Answer
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1 Answer
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$begingroup$
According to the research paper, neoperceptrons are a class of CNN that are not sensitive to rotations.
One of the issues with traditional kernels (that was the case before CNN and it is still true with them) is that the rotation of the input image would lead to different results, because the neurons in the dense layer would have different levels of activations.
With these new neurons, you don't get an issue with orientation. So in theory, if you have a gradient in your image, no matter what the orientation is, you would get the same value.
For a traditional CNN, you would get maximum activation with the original orientation, inverse with a 180° rotated image, and no activation with 90° or 270°.
answered Oct 26 '18 at 8:24
Matthieu BrucherMatthieu Brucher
69113
$endgroup$
$begingroup$
Thank you very much for explanation! Everything fell into place
$endgroup$
– Шах
Oct 26 '18 at 8:29
$begingroup$
Which research paper are you referring to?
$endgroup$
– nbro
yesterday
add a comment |
$begingroup$
According to the research paper, neoperceptrons are a class of CNN that are not sensitive to rotations.
One of the issues with traditional kernels (that was the case before CNN and it is still true with them) is that the rotation of the input image would lead to different results, because the neurons in the dense layer would have different levels of activations.
With these new neurons, you don't get an issue with orientation. So in theory, if you have a gradient in your image, no matter what the orientation is, you would get the same value.
For a traditional CNN, you would get maximum activation with the original orientation, inverse with a 180° rotated image, and no activation with 90° or 270°.
answered Oct 26 '18 at 8:24
Matthieu BrucherMatthieu Brucher
69113
$endgroup$
$begingroup$
Thank you very much for explanation! Everything fell into place
$endgroup$
– Шах
Oct 26 '18 at 8:29
$begingroup$
Which research paper are you referring to?
$endgroup$
– nbro
yesterday
add a comment |
$begingroup$
According to the research paper, neoperceptrons are a class of CNN that are not sensitive to rotations.
One of the issues with traditional kernels (that was the case before CNN and it is still true with them) is that the rotation of the input image would lead to different results, because the neurons in the dense layer would have different levels of activations.
With these new neurons, you don't get an issue with orientation. So in theory, if you have a gradient in your image, no matter what the orientation is, you would get the same value.
For a traditional CNN, you would get maximum activation with the original orientation, inverse with a 180° rotated image, and no activation with 90° or 270°.
answered Oct 26 '18 at 8:24
Matthieu BrucherMatthieu Brucher
69113
$endgroup$
According to the research paper, neoperceptrons are a class of CNN that are not sensitive to rotations.
One of the issues with traditional kernels (that was the case before CNN and it is still true with them) is that the rotation of the input image would lead to different results, because the neurons in the dense layer would have different levels of activations.
With these new neurons, you don't get an issue with orientation. So in theory, if you have a gradient in your image, no matter what the orientation is, you would get the same value.
For a traditional CNN, you would get maximum activation with the original orientation, inverse with a 180° rotated image, and no activation with 90° or 270°.
answered Oct 26 '18 at 8:24
Matthieu BrucherMatthieu Brucher
69113
answered Oct 26 '18 at 8:24
Matthieu BrucherMatthieu Brucher
69113
answered Oct 26 '18 at 8:24
Matthieu BrucherMatthieu Brucher
69113
answered Oct 26 '18 at 8:24
Matthieu BrucherMatthieu Brucher
69113
69113
$begingroup$
Thank you very much for explanation! Everything fell into place
$endgroup$
– Шах
Oct 26 '18 at 8:29
$begingroup$
Which research paper are you referring to?
$endgroup$
– nbro
yesterday
add a comment |
$begingroup$
Thank you very much for explanation! Everything fell into place
$endgroup$
– Шах
Oct 26 '18 at 8:29
$begingroup$
Which research paper are you referring to?
$endgroup$
– nbro
yesterday
$begingroup$
Thank you very much for explanation! Everything fell into place
$endgroup$
– Шах
Oct 26 '18 at 8:29
$begingroup$
Thank you very much for explanation! Everything fell into place
$endgroup$
– Шах
Oct 26 '18 at 8:29
$begingroup$
Which research paper are you referring to?
$endgroup$
– nbro
yesterday
$begingroup$
Which research paper are you referring to?
$endgroup$
– nbro
yesterday
add a comment |
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