71 citations · 98 across the 32 of their papers we have counts for
11 papers · 1 filter
PIPE : Parallelized Inference Through Post-Training Quantization Ensembling of Residual Expansions
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
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…
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…
MultIOD: Rehearsal-free Multihead Incremental Object Detector
Eden Belouadah, Arnaud Dapogny, Kevin Bailly
Class-Incremental learning (CIL) refers to the ability of artificial agents to integrate new classes as they appear in a stream. It is particularly interesting in evolving environm…
Gradient-Based Post-Training Quantization: Challenging the Status Quo
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
Quantization has become a crucial step for the efficient deployment of deep neural networks, where floating point operations are converted to simpler fixed point operations. In its…
NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
Deep neural network (DNN) deployment has been confined to larger hardware devices due to their expensive computational requirements. This challenge has recently reached another sca…
SAfER: Layer-Level Sensitivity Assessment for Efficient and Robust Neural Network Inference
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly +1
Deep neural networks (DNNs) demonstrate outstanding performance across most computer vision tasks. Some critical applications, such as autonomous driving or medical imaging, also r…
Designing strong baselines for ternary neural network quantization through support and mass equalization
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
Deep neural networks (DNNs) offer the highest performance in a wide range of applications in computer vision. These results rely on over-parameterized backbones, which are expensiv…
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…