1 citations · 2 across the 2 of their papers we have counts for
5 papers
Improving Convolutional Neural Networks Via Conservative Field Regularisation and Integration
Dominique Beaini, Sofiane Achiche, Maxime Raison
Current research in convolutional neural networks (CNN) focuses mainly on changing the architecture of the networks, optimizing the hyper-parameters and improving the gradient desc…
Saliency Enhancement using Gradient Domain Edges Merging
Dominique Beaini, Sofiane Achiche, Alexandre Duperre +1
In recent years, there has been a rapid progress in solving the binary problems in computer vision, such as edge detection which finds the boundaries of an image and salient object…
Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks
Dominique Beaini, Sofiane Achiche, Alexandre Duperré +1
Current saliency methods require to learn large scale regional features using small convolutional kernels, which is not possible with a simple feed-forward network. Some methods so…
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
Emmanuel Noutahi, Dominique Beaini, Julien Horwood +2
Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks. Unfortunately, GNNs often fail to capture the relative impo…
Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolution
Dominique Beaini, Sofiane Achiche, Fabrice Nonez +4
In computer vision, the gradient and Laplacian of an image are used in different applications, such as edge detection, feature extraction, and seamless image cloning. Computing the…