4 citations · 5 across the 8 of their papers we have counts for
11 papers
Neural Networks on Symmetric Spaces of Noncompact Type
Xuan Son Nguyen, Shuo Yang, Aymeric Histace
Recent works have demonstrated promising performances of neural networks on hyperbolic spaces and symmetric positive definite (SPD) manifolds. These spaces belong to a family of Ri…
Siegel Neural Networks
Xuan Son Nguyen, Aymeric Histace, Nistor Grozavu
Riemannian symmetric spaces (RSS) such as hyperbolic spaces and symmetric positive definite (SPD) manifolds have become popular spaces for representation learning. In this paper, w…
Matrix Manifold Neural Networks++
Xuan Son Nguyen, Shuo Yang, Aymeric Histace
Deep neural networks (DNNs) on Riemannian manifolds have garnered increasing interest in various applied areas. For instance, DNNs on spherical and hyperbolic manifolds have been d…
Anomaly Detection via Multi-Scale Contrasted Memory
Loic Jezequel, Ngoc-Son Vu, Jean Beaudet +1
Deep anomaly detection (AD) aims to provide robust and efficient classifiers for one-class and unbalanced settings. However current AD models still struggle on edge-case normal sam…
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks
Loic Jezequel, Ngoc-Son Vu, Jean Beaudet +1
Anomaly detection is important in many real-life applications. Recently, self-supervised learning has greatly helped deep anomaly detection by recognizing several geometric transfo…
Fine-grained Anomaly Detection via Multi-task Self-Supervision
Loic Jezequel, Ngoc-Son Vu, Jean Beaudet +1
Detecting anomalies using deep learning has become a major challenge over the last years, and is becoming increasingly promising in several fields. The introduction of self-supervi…