14 citations · 14 across the 4 of their papers we have counts for
4 papers
A Recipe for Efficient SBIR Models: Combining Relative Triplet Loss with Batch Normalization and Knowledge Distillation
Omar Seddati, Nathan Hubens, Stéphane Dupont +1
Sketch-Based Image Retrieval (SBIR) is a crucial task in multimedia retrieval, where the goal is to retrieve a set of images that match a given sketch query. Researchers have alrea…
Induced Feature Selection by Structured Pruning
Nathan Hubens, Victor Delvigne, Matei Mancas +3
The advent of sparsity inducing techniques in neural networks has been of a great help in the last few years. Indeed, those methods allowed to find lighter and faster networks, abl…
FasterAI: A Lightweight Library for Creating Sparse Neural Networks
Nathan Hubens
FasterAI is a PyTorch-based library, aiming to facilitate the utilization of deep neural networks compression techniques such as sparsification, pruning, knowledge distillation, or…
An Experimental Study of the Impact of Pre-training on the Pruning of a Convolutional Neural Network
Nathan Hubens, Matei Mancas, Bernard Gosselin +2
In recent years, deep neural networks have known a wide success in various application domains. However, they require important computational and memory resources, which severely h…