71 citations · 75 across the 9 of their papers we have counts for
7 papers · 1 filter
Archtree: on-the-fly tree-structured exploration for latency-aware pruning of deep neural networks
Rémi Ouazan Reboul, Edouard Yvinec, Arnaud Dapogny +1
Deep neural networks (DNNs) have become ubiquitous in addressing a number of problems, particularly in computer vision. However, DNN inference is computationally intensive, which c…
Network Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
The massive interest in deep neural networks (DNNs) for both computer vision and natural language processing has been sparked by the growth in computational power. However, this le…
Fighting over-fitting with quantization for learning deep neural networks on noisy labels
Gauthier Tallec, Edouard Yvinec, Arnaud Dapogny +1
The rising performance of deep neural networks is often empirically attributed to an increase in the available computational power, which allows complex models to be trained upon l…
Fighting noise and imbalance in Action Unit detection problems
Gauthier Tallec, Arnaud Dapogny, Kevin Bailly
Action Unit (AU) detection aims at automatically caracterizing facial expressions with the muscular activations they involve. Its main interest is to provide a low-level face repre…
PowerQuant: Automorphism Search for Non-Uniform Quantization
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Deep neural networks (DNNs) are nowadays ubiquitous in many domains such as computer vision. However, due to their high latency, the deployment of DNNs hinges on the development of…
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…