221 citations · 507 across the 42 of their papers we have counts for
6 papers · 1 filter
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…
Sparse Double Descent in Vision Transformers: real or phantom threat?
Victor Quétu, Marta Milovanovic, Enzo Tartaglione
Vision transformers (ViT) have been of broad interest in recent theoretical and empirical works. They are state-of-the-art thanks to their attention-based approach, which boosts th…
Compressing Explicit Voxel Grid Representations: fast NeRFs become also small
Chenxi Lola Deng, Enzo Tartaglione
NeRFs have revolutionized the world of per-scene radiance field reconstruction because of their intrinsic compactness. One of the main limitations of NeRFs is their slow rendering…
Capsule Networks Do Not Need to Model Everything
Riccardo Renzulli, Enzo Tartaglione, Marco Grangetto
Capsule networks are biologically inspired neural networks that group neurons into vectors called capsules, each explicitly representing an object or one of its parts. The routing…
EnD: Entangling and Disentangling deep representations for bias correction
Enzo Tartaglione, Carlo Alberto Barbano, Marco Grangetto
Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and nowadays they are used to solve an incredibly large variety of tasks. There are problems…
A non-discriminatory approach to ethical deep learning
Enzo Tartaglione, Marco Grangetto
Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, nowadays they are used to solve an incredibly large variety of tasks. However, typical train…