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Found 15 papers, 6 papers with code
A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
The distribution of the weights of modern deep neural networks (DNNs) - crucial for uncertainty quantification and robustness - is an eminently complex object due to its extremely high dimensionality.
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.
Learning a Discriminant Latent Space with Neural Discriminant Analysis
NDA transforms deep features to become more discriminative and, therefore, improves the performances in various tasks.
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification
Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks.
Ranked #132 on
Image Classification
on CIFAR-10
One Versus all for deep Neural Network Incertitude (OVNNI) quantification
This is due to the fact that modern DNNs are usually uncalibrated and we cannot characterize their epistemic uncertainty.
TRADI: Tracking deep neural network weight distributions for uncertainty estimation
During training, the weights of a Deep Neural Network (DNN) are optimized from a random initialization towards a nearly optimum value minimizing a loss function.
Evaluating Crowd Density Estimators via Their Uncertainty Bounds
In this work, we use the Belief Function Theory which extends the probabilistic framework in order to provide uncertainty bounds to different categories of crowd density estimators.
Geometry-Based Multiple Camera Head Detection in Dense Crowds
This paper addresses the problem of head detection in crowded environments.
Efficient Evaluation of the Number of False Alarm Criterion
In this work, we introduce a strategy which relies on the use of a cumulative space of reduced dimensionality, derived from the coupling of a classic (Hough) cumulative space with an integral histogram trick.
2CoBel : An Efficient Belief Function Extension for Two-dimensional Continuous Spaces
This paper introduces an innovative approach for handling 2D compound hypotheses within the Belief Function Theory framework.
Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
In this paper we address the problem of multiple camera calibration in the presence of a homogeneous scene, and without the possibility of employing calibration object based methods.
