221 citations · 474 across the 39 of their papers we have counts for
43 papers
Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning
Le-Trung Nguyen, Ael Quelennec, Van-Tam Nguyen +1
On-device learning has emerged as a promising direction for AI development, particularly because of its potential to reduce latency issues and mitigate privacy risks associated wit…
Efficient Adaptation of Deep Neural Networks for Semantic Segmentation in Space Applications
Leonardo Olivi, Edoardo Santero Mormile, Enzo Tartaglione
In recent years, the application of Deep Learning techniques has shown remarkable success in various computer vision tasks, paving the way for their deployment in extraterrestrial…
Till the Layers Collapse: Compressing a Deep Neural Network through the Lenses of Batch Normalization Layers
Zhu Liao, Nour Hezbri, Victor Quétu +2
Today, deep neural networks are widely used since they can handle a variety of complex tasks. Their generality makes them very powerful tools in modern technology. However, deep ne…
Activation Map Compression through Tensor Decomposition for Deep Learning
Le-Trung Nguyen, Aël Quélennec, Enzo Tartaglione +2
Internet of Things and Deep Learning are synergetically and exponentially growing industrial fields with a massive call for their unification into a common framework called Edge AI…
Memory-Optimized Once-For-All Network
Maxime Girard, Victor Quétu, Samuel Tardieu +2
Deploying Deep Neural Networks (DNNs) on different hardware platforms is challenging due to varying resource constraints. Besides handcrafted approaches aiming at making deep model…
LaCoOT: Layer Collapse through Optimal Transport
Victor Quétu, Zhu Liao, Nour Hezbri +2
Although deep neural networks are well-known for their outstanding performance in tackling complex tasks, their hunger for computational resources remains a significant hurdle, pos…