6 citations · 16 across the 17 of their papers we have counts for
20 papers
Lightweight Graph Convolutional Networks with Topologically Consistent Magnitude Pruning
Hichem Sahbi
Graph convolution networks (GCNs) are currently mainstream in learning with irregular data. These models rely on message passing and attention mechanisms that capture context and n…
Reinforcement-based frugal learning for satellite image change detection
Sebastien Deschamps, Hichem Sahbi
In this paper, we introduce a novel interactive satellite image change detection algorithm based on active learning. The proposed approach is iterative and asks the user (oracle) q…
Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection
Hichem Sahbi, Sebastien Deschamps
In this paper, we devise a novel interactive satellite image change detection algorithm based on active learning. The proposed framework is iterative and relies on a question and a…
Active learning for interactive satellite image change detection
Hichem Sahbi, Sebastien Deschamps, Andrei Stoian
We introduce in this paper a novel active learning algorithm for satellite image change detection. The proposed solution is interactive and based on a question and answer model, wh…
Weight Reparametrization for Budget-Aware Network Pruning
Robin Dupont, Hichem Sahbi, Guillaume Michel
Pruning seeks to design lightweight architectures by removing redundant weights in overparameterized networks. Most of the existing techniques first remove structured sub-networks…
Action Recognition with Kernel-based Graph Convolutional Networks
Hichem Sahbi
Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing deep learning to arbitrary non-regular domains. Most of the existing GCNs follow a neig…