87 citations · 96 across the 9 of their papers we have counts for
13 papers
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…
ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results
Nina Miolane, Matteo Caorsi, Umberto Lupo +30
This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning…
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection
Martin Bauw, Santiago Velasco-Forero, Jesus Angulo +2
Random projection is a common technique for designing algorithms in a variety of areas, including information retrieval, compressive sensing and measuring of outlyingness. In this…
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets
Martin Bauw, Santiago Velasco-Forero, Jesus Angulo +2
Responding to the challenge of detecting unusual radar targets in a well identified environment, innovative anomaly and novelty detection methods keep emerging in the literature. T…
Scale Equivariant Neural Networks with Morphological Scale-Spaces
Mateus Sangalli, Samy Blusseau, Santiago Velasco-Forero +1
The translation equivariance of convolutions can make convolutional neural networks translation equivariant or invariant. Equivariance to other transformations (e.g. rotations, aff…