11 citations · 15 across the 10 of their papers we have counts for
14 papers
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.…
RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Pruning Deep Neural Networks (DNNs) is a prominent field of study in the goal of inference runtime acceleration. In this paper, we introduce a novel data-free pruning protocol RED+…
Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation
Arthur Douillard, Yifu Chen, Arnaud Dapogny +1
Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power.…
RED : Looking for Redundancies for Data-Free Structured Compression of Deep Neural Networks
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Deep Neural Networks (DNNs) are ubiquitous in today's computer vision land-scape, despite involving considerable computational costs. The mainstream approaches for runtime accelera…
PLOP: Learning without Forgetting for Continual Semantic Segmentation
Arthur Douillard, Yifu Chen, Arnaud Dapogny +1
Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power.…