65 citations · 171 across the 25 of their papers we have counts for
5 papers · 1 filter
SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
The leap in performance in state-of-the-art computer vision methods is attributed to the development of deep neural networks. However it often comes at a computational price which…
To Fold or Not to Fold: a Necessary and Sufficient Condition on Batch-Normalization Layers Folding
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
Batch-Normalization (BN) layers have become fundamental components in the evermore complex deep neural network architectures. Such models require acceleration processes for deploym…
SPIQ: Data-Free Per-Channel Static Input Quantization
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Computationally expensive neural networks are ubiquitous in computer vision and solutions for efficient inference have drawn a growing attention in the machine learning community.…
REx: Data-Free Residual Quantization Error Expansion
Edouard Yvinec, Arnaud Dapgony, Matthieu Cord +1
Deep neural networks (DNNs) are ubiquitous in computer vision and natural language processing, but suffer from high inference cost. This problem can be addressed by quantization, w…
Multi-label Transformer for Action Unit Detection
Gauthier Tallec, Edouard Yvinec, Arnaud Dapogny +1
Action Unit (AU) Detection is the branch of affective computing that aims at recognizing unitary facial muscular movements. It is key to unlock unbiased computational face represen…