Search Results for author:
Found 8 papers, 6 papers with code
SURE: SUrvey REcipes for building reliable and robust deep networks
In this paper, we revisit techniques for uncertainty estimation within deep neural networks and consolidate a suite of techniques to enhance their reliability.
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Learning with noisy labels
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InfraParis: A multi-modal and multi-task autonomous driving dataset
Current deep neural networks (DNNs) for autonomous driving computer vision are typically trained on specific datasets that only involve a single type of data and urban scenes.
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
In this work, we propose a generalized AuxUE scheme for more robust uncertainty quantification on regression tasks.
Latent Discriminant deterministic Uncertainty
Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-world autonomous systems.
MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks
However, disentangling the different types and sources of uncertainty is non trivial for most datasets, especially since there is no ground truth for uncertainty.
On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression
To this end, this paper will introduce a taxonomy and summary of CAR approaches, a new uncertainty estimation solution for CAR, and a set of experiments on depth accuracy and uncertainty quantification for CAR-based models on KITTI dataset.
SLURP: Side Learning Uncertainty for Regression Problems
It has become critical for deep learning algorithms to quantify their output uncertainties to satisfy reliability constraints and provide accurate results.
