Add to ORKGIn many information processing systems, it may be desirable to ensure that any change of the input, whether by shifting or scaling, results in a corresponding change in the system response. While deep neural networks are gradually replacing all traditional automatic processing methods, they surprisingly do not guarantee such normalization-equivariance (scale + shift) property, which can be detrimental in many applications. To address this issue, we propose a methodology for adapting existing neural networks so that normalization-equivariance holds by design. Our main claim is that not only ordinary convolutional layers, but also all activation functions, including the ReLU (rectified linear unit), which are applied element-wise to the pre-a...
Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s input...
Image denoising can be described as the problem of mapping from a noisy image to a noise-free image....
Many application domains, spanning from low-level computer vision to medical imaging, require high-f...
In this paper, we propose a novel convolutional neural network (CNN) for image denoising, which uses...
International audienceModern neural networks are over-parametrized. In particular, each rectified li...
Image noise degrades the performance of various imaging applications including medical imaging, astr...
Various normalization layers have been proposed to help the training of neural networks. Group Norma...
Owing to the flexible architectures of deep convolutional neural networks (CNNs) are successfully us...
The success of deep neural networks is in part due to the use of normalization layers. Normalization...
Batch Normalization (BN) is an essential component of the Deep Neural Networks (DNNs) architectures....
While modern convolutional neural networks achieve outstanding accuracy on many image classification...
Batch Normalization (BatchNorm) is a technique that enables the training of deep neural networks, es...
Inspired by the adaptation phenomenon of neuronal firing, we propose the regularity normalization (R...
High image quality is desirable in fields like in the medical field where image analysis is often pe...
Deep Residual Networks (ResNets) have recently achieved state-of-the-art results on many challenging...
Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s input...
Image denoising can be described as the problem of mapping from a noisy image to a noise-free image....
Many application domains, spanning from low-level computer vision to medical imaging, require high-f...
In this paper, we propose a novel convolutional neural network (CNN) for image denoising, which uses...
International audienceModern neural networks are over-parametrized. In particular, each rectified li...
Image noise degrades the performance of various imaging applications including medical imaging, astr...
Various normalization layers have been proposed to help the training of neural networks. Group Norma...
Owing to the flexible architectures of deep convolutional neural networks (CNNs) are successfully us...
The success of deep neural networks is in part due to the use of normalization layers. Normalization...
Batch Normalization (BN) is an essential component of the Deep Neural Networks (DNNs) architectures....
While modern convolutional neural networks achieve outstanding accuracy on many image classification...
Batch Normalization (BatchNorm) is a technique that enables the training of deep neural networks, es...
Inspired by the adaptation phenomenon of neuronal firing, we propose the regularity normalization (R...
High image quality is desirable in fields like in the medical field where image analysis is often pe...
Deep Residual Networks (ResNets) have recently achieved state-of-the-art results on many challenging...
Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s input...
Image denoising can be described as the problem of mapping from a noisy image to a noise-free image....
Many application domains, spanning from low-level computer vision to medical imaging, require high-f...