20 citations · 40 across the 46 of their papers we have counts for
6 papers · 1 filter
One-Shot Multi-Rate Pruning of Graph Convolutional Networks
Hichem Sahbi
In this paper, we devise a novel lightweight Graph Convolutional Network (GCN) design dubbed as Multi-Rate Magnitude Pruning (MRMP) that jointly trains network topology and weights…
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection
Hichem Sahbi
We introduce a novel interactive satellite image change detection algorithm based on active learning. The proposed method is iterative and consists in frugally probing the user (or…
MonoProb: Self-Supervised Monocular Depth Estimation with Interpretable Uncertainty
Rémi Marsal, Florian Chabot, Angelique Loesch +2
Self-supervised monocular depth estimation methods aim to be used in critical applications such as autonomous vehicles for environment analysis. To circumvent the potential imperfe…
Frugal Satellite Image Change Detection with Deep-Net Inversion
Hichem Sahbi, Sebastien Deschamps
Change detection in satellite imagery seeks to find occurrences of targeted changes in a given scene taken at different instants. This task has several applications ranging from la…
Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning
Hichem Sahbi
Magnitude pruning is one of the mainstream methods in lightweight architecture design whose goal is to extract subnetworks with the largest weight connections. This method is known…
Budget-Aware Graph Convolutional Network Design using Probabilistic Magnitude Pruning
Hichem Sahbi
Graph convolutional networks (GCNs) are nowadays becoming mainstream in solving many image processing tasks including skeleton-based recognition. Their general recipe consists in l…