154 citations · 334 across the 20 of their papers we have counts for
7 papers · 1 filter
Moving Frame Net: SE(3)-Equivariant Network for Volumes
Mateus Sangalli, Samy Blusseau, Santiago Velasco-Forero +1
Equivariance of neural networks to transformations helps to improve their performance and reduce generalization error in computer vision tasks, as they apply to datasets presenting…
Scale Equivariant U-Net
Mateus Sangalli, Samy Blusseau, Santiago Velasco-Forero +1
In neural networks, the property of being equivariant to transformations improves generalization when the corresponding symmetry is present in the data. In particular, scale-equiva…
Differential invariants for SE(2)-equivariant networks
Mateus Sangalli, Samy Blusseau, Santiago Velasco-Forero +1
Symmetry is present in many tasks in computer vision, where the same class of objects can appear transformed, e.g. rotated due to different camera orientations, or scaled due to pe…
Fully trainable Gaussian derivative convolutional layer
Valentin Penaud--Polge, Santiago Velasco-Forero, Jesus Angulo
The Gaussian kernel and its derivatives have already been employed for Convolutional Neural Networks in several previous works. Most of these papers proposed to compute filters by…
MorphoActivation: Generalizing ReLU activation function by mathematical morphology
Santiago Velasco-Forero, Jesús Angulo
This paper analyses both nonlinear activation functions and spatial max-pooling for Deep Convolutional Neural Networks (DCNNs) by means of the algebraic basis of mathematical morph…
Morphological adjunctions represented by matrices in max-plus algebra for signal and image processing
Samy Blusseau, Santiago Velasco-Forero, Jesus Angulo +1
In discrete signal and image processing, many dilations and erosions can be written as the max-plus and min-plus product of a matrix on a vector. Previous studies considered operat…
Near out-of-distribution detection for low-resolution radar micro-Doppler signatures
Martin Bauw, Santiago Velasco-Forero, Jesus Angulo +2
Near out-of-distribution detection (OODD) aims at discriminating semantically similar data points without the supervision required for classification. This paper puts forward an OO…