13 citations · 41 across the 15 of their papers we have counts for
16 papers
ICPR 2024 Competition on Domain Adaptation and GEneralization for Character Classification (DAGECC)
Sofia Marino, Jennifer Vandoni, Emanuel Aldea +3
In this companion paper for the DAGECC (Domain Adaptation and GEneralization for Character Classification) competition organized within the frame of the ICPR 2024 conference, we pr…
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
Xuanlong Yu, Gianni Franchi, Jindong Gu +1
Uncertainty quantification is critical for deploying deep neural networks (DNNs) in real-world applications. An Auxiliary Uncertainty Estimator (AuxUE) is one of the most effective…
Latent Discriminant deterministic Uncertainty
Gianni Franchi, Xuanlong Yu, Andrei Bursuc +3
Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-world autonomous systems. However, most successful approaches are computationally intensiv…
MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks
Gianni Franchi, Xuanlong Yu, Andrei Bursuc +5
Predictive uncertainty estimation is essential for safe deployment of Deep Neural Networks in real-world autonomous systems. However, disentangling the different types and sources…
On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression
Xuanlong Yu, Gianni Franchi, Emanuel Aldea
Monocular depth is important in many tasks, such as 3D reconstruction and autonomous driving. Deep learning based models achieve state-of-the-art performance in this field. A set o…
SLURP: Side Learning Uncertainty for Regression Problems
Xuanlong Yu, Gianni Franchi, Emanuel Aldea
It has become critical for deep learning algorithms to quantify their output uncertainties to satisfy reliability constraints and provide accurate results. Uncertainty estimation f…