What does it mean to be “stable to deformation”?
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In the context of image classification, what does it mean to be stable to deformation? Say I were trying to classify digits, what would the difference be between an operation that is stable vs unstable to deformation?
neural-network image-classification
asked yesterday
IzzoIzzo
1112
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add a comment |
$begingroup$
In the context of image classification, what does it mean to be stable to deformation? Say I were trying to classify digits, what would the difference be between an operation that is stable vs unstable to deformation?
neural-network image-classification
asked yesterday
IzzoIzzo
1112
$endgroup$
$begingroup$
Could you provide some context? I think you are talking about afine transformations and models that are robust to such transformations, but I can't be sure. Where did you see this?
$endgroup$
– Pedro Henrique Monforte
yesterday
add a comment |
$begingroup$
In the context of image classification, what does it mean to be stable to deformation? Say I were trying to classify digits, what would the difference be between an operation that is stable vs unstable to deformation?
neural-network image-classification
asked yesterday
IzzoIzzo
1112
$endgroup$
In the context of image classification, what does it mean to be stable to deformation? Say I were trying to classify digits, what would the difference be between an operation that is stable vs unstable to deformation?
neural-network image-classification
neural-network image-classification
asked yesterday
IzzoIzzo
1112
asked yesterday
IzzoIzzo
1112
asked yesterday
IzzoIzzo
1112
asked yesterday
IzzoIzzo
1112
asked yesterday
IzzoIzzo
1112
1112
$begingroup$
Could you provide some context? I think you are talking about afine transformations and models that are robust to such transformations, but I can't be sure. Where did you see this?
$endgroup$
– Pedro Henrique Monforte
yesterday
add a comment |
$begingroup$
Could you provide some context? I think you are talking about afine transformations and models that are robust to such transformations, but I can't be sure. Where did you see this?
$endgroup$
– Pedro Henrique Monforte
yesterday
$begingroup$
Could you provide some context? I think you are talking about afine transformations and models that are robust to such transformations, but I can't be sure. Where did you see this?
$endgroup$
– Pedro Henrique Monforte
yesterday
$begingroup$
Could you provide some context? I think you are talking about afine transformations and models that are robust to such transformations, but I can't be sure. Where did you see this?
$endgroup$
– Pedro Henrique Monforte
yesterday
add a comment |
1 Answer
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Images are susceptible to deformations, i.e. afine or arbitrary deformations, such as "melting" effects.
In computer vision algorithms for non-constrained environments it may be desirable for a predictive model to be "stable", i.e. robust, ideally invariant, to arbitrary transformations that are common in that problem. E.g. a digit classifier would benefit from been stable to common paper deformations such as those caused by wrapping and unwrapping a long letter.
It is said that pooling layers insert certain stability to these deformations, this is analysed by Ruderman on his paper Learned Deformation Stability in Convolutional Neural Networks and in Pooling is neither necessary nor sufficient for
appropriate deformation stability in CNNs.
Some features, such as SIFT, BRISK and HoG try to deal with the most common deformations in image (Scale and Rotation). Most of convolution-dependent methods are already invariant to shifting as that is a feature of convolutional filtering itself.
answered yesterday
Pedro Henrique MonfortePedro Henrique Monforte
424112
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add a comment |
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1 Answer
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1 Answer
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active
oldest
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oldest
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active
oldest
votes
$begingroup$
Images are susceptible to deformations, i.e. afine or arbitrary deformations, such as "melting" effects.
In computer vision algorithms for non-constrained environments it may be desirable for a predictive model to be "stable", i.e. robust, ideally invariant, to arbitrary transformations that are common in that problem. E.g. a digit classifier would benefit from been stable to common paper deformations such as those caused by wrapping and unwrapping a long letter.
It is said that pooling layers insert certain stability to these deformations, this is analysed by Ruderman on his paper Learned Deformation Stability in Convolutional Neural Networks and in Pooling is neither necessary nor sufficient for
appropriate deformation stability in CNNs.
Some features, such as SIFT, BRISK and HoG try to deal with the most common deformations in image (Scale and Rotation). Most of convolution-dependent methods are already invariant to shifting as that is a feature of convolutional filtering itself.
answered yesterday
Pedro Henrique MonfortePedro Henrique Monforte
424112
$endgroup$
add a comment |
$begingroup$
Images are susceptible to deformations, i.e. afine or arbitrary deformations, such as "melting" effects.
In computer vision algorithms for non-constrained environments it may be desirable for a predictive model to be "stable", i.e. robust, ideally invariant, to arbitrary transformations that are common in that problem. E.g. a digit classifier would benefit from been stable to common paper deformations such as those caused by wrapping and unwrapping a long letter.
It is said that pooling layers insert certain stability to these deformations, this is analysed by Ruderman on his paper Learned Deformation Stability in Convolutional Neural Networks and in Pooling is neither necessary nor sufficient for
appropriate deformation stability in CNNs.
Some features, such as SIFT, BRISK and HoG try to deal with the most common deformations in image (Scale and Rotation). Most of convolution-dependent methods are already invariant to shifting as that is a feature of convolutional filtering itself.
answered yesterday
Pedro Henrique MonfortePedro Henrique Monforte
424112
$endgroup$
add a comment |
$begingroup$
Images are susceptible to deformations, i.e. afine or arbitrary deformations, such as "melting" effects.
In computer vision algorithms for non-constrained environments it may be desirable for a predictive model to be "stable", i.e. robust, ideally invariant, to arbitrary transformations that are common in that problem. E.g. a digit classifier would benefit from been stable to common paper deformations such as those caused by wrapping and unwrapping a long letter.
It is said that pooling layers insert certain stability to these deformations, this is analysed by Ruderman on his paper Learned Deformation Stability in Convolutional Neural Networks and in Pooling is neither necessary nor sufficient for
appropriate deformation stability in CNNs.
Some features, such as SIFT, BRISK and HoG try to deal with the most common deformations in image (Scale and Rotation). Most of convolution-dependent methods are already invariant to shifting as that is a feature of convolutional filtering itself.
answered yesterday
Pedro Henrique MonfortePedro Henrique Monforte
424112
$endgroup$
Images are susceptible to deformations, i.e. afine or arbitrary deformations, such as "melting" effects.
In computer vision algorithms for non-constrained environments it may be desirable for a predictive model to be "stable", i.e. robust, ideally invariant, to arbitrary transformations that are common in that problem. E.g. a digit classifier would benefit from been stable to common paper deformations such as those caused by wrapping and unwrapping a long letter.
It is said that pooling layers insert certain stability to these deformations, this is analysed by Ruderman on his paper Learned Deformation Stability in Convolutional Neural Networks and in Pooling is neither necessary nor sufficient for
appropriate deformation stability in CNNs.
Some features, such as SIFT, BRISK and HoG try to deal with the most common deformations in image (Scale and Rotation). Most of convolution-dependent methods are already invariant to shifting as that is a feature of convolutional filtering itself.
answered yesterday
Pedro Henrique MonfortePedro Henrique Monforte
424112
answered yesterday
Pedro Henrique MonfortePedro Henrique Monforte
424112
answered yesterday
Pedro Henrique MonfortePedro Henrique Monforte
424112
answered yesterday
Pedro Henrique MonfortePedro Henrique Monforte
424112
424112
add a comment |
add a comment |
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$begingroup$
Could you provide some context? I think you are talking about afine transformations and models that are robust to such transformations, but I can't be sure. Where did you see this?
$endgroup$
– Pedro Henrique Monforte
yesterday