Add to ORKGWe show that spatial transformations of CNN feature maps cannot align the feature maps of a transformed image to match those of it’s original for general affine transformations. This implies that methods that spatially transform CNN feature maps, such as spatial transformer networks, dilated or deformable convolutions or spatial pyramid pooling cannot enable true invariance. Our proof is based on elementary analysis for both the single- and multi-layer network cases.QC 20201224</p
Yes, convolutional neural networks are domain-invariant, albeit to some limited extent. We explored ...
In this thesis we have looked into the complexity of neural networks. Especially convolutional neura...
National audienceOver the past decade, some progress has been made on understanding the strengths an...
A large number of deep learning architectures use spatial transformations of CNN feature maps or fil...
Spatial transformer networks (STNs) were designed to enable convolutional neural networks (CNNs) to ...
Spatial transformer networks (STNs) were designed to enable CNNs to learn invariance to image transf...
Convolutional Neural Networks (CNNs) are extremely efficient, since they exploit the inherent transl...
Introducing variation in the training dataset through data augmentation has been a popular technique...
This thesis addresses the problem of investigating the properties and abilities of a variety of comp...
© 2016 ACM. Convolutional neural networks (CNNs) have achieved stateof-the-art results on many visua...
Existing techniques to encode spatial invariance within deep convolutional neural networks (CNNs) ap...
Despite the importance of image representations such as histograms of oriented gradients and deep Co...
An important goal in visual recognition is to devise image representations that are invariant to par...
Despite the importance of image representations such as histograms of oriented gradients and deep Co...
One approach to computer object recognition and modeling the brain’s ventral stream involves unsuper...
Yes, convolutional neural networks are domain-invariant, albeit to some limited extent. We explored ...
In this thesis we have looked into the complexity of neural networks. Especially convolutional neura...
National audienceOver the past decade, some progress has been made on understanding the strengths an...
A large number of deep learning architectures use spatial transformations of CNN feature maps or fil...
Spatial transformer networks (STNs) were designed to enable convolutional neural networks (CNNs) to ...
Spatial transformer networks (STNs) were designed to enable CNNs to learn invariance to image transf...
Convolutional Neural Networks (CNNs) are extremely efficient, since they exploit the inherent transl...
Introducing variation in the training dataset through data augmentation has been a popular technique...
This thesis addresses the problem of investigating the properties and abilities of a variety of comp...
© 2016 ACM. Convolutional neural networks (CNNs) have achieved stateof-the-art results on many visua...
Existing techniques to encode spatial invariance within deep convolutional neural networks (CNNs) ap...
Despite the importance of image representations such as histograms of oriented gradients and deep Co...
An important goal in visual recognition is to devise image representations that are invariant to par...
Despite the importance of image representations such as histograms of oriented gradients and deep Co...
One approach to computer object recognition and modeling the brain’s ventral stream involves unsuper...
Yes, convolutional neural networks are domain-invariant, albeit to some limited extent. We explored ...
In this thesis we have looked into the complexity of neural networks. Especially convolutional neura...
National audienceOver the past decade, some progress has been made on understanding the strengths an...