13 citations · 41 across the 14 of their papers we have counts for
4 papers · 1 filter
Packed-Ensembles for Efficient Uncertainty Estimation
Olivier Laurent, Adrien Lafage, Enzo Tartaglione +4
Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detec…
Instance-Aware Observer Network for Out-of-Distribution Object Segmentation
Victor Besnier, Andrei Bursuc, David Picard +1
Recent works on predictive uncertainty estimation have shown promising results on Out-Of-Distribution (OOD) detection for semantic segmentation. However, these methods struggle to…
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…