Gradient Based Quantification Of Epistemic Uncertainty For Deep Object Detectors

June 1, 2026
Main Context: View more information on the DOE CSGF Program at Brian Lockwood University of Wyoming With the ... Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection, CoRL2022

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Despite tremendous success of modern neural networks, they are known to be overconfident even when the model encounters ... Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection, CoRL2022

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Authors: Riedlinger, Tobias*; Rottmann, Matthias; Schubert, Marius; Gottschalk, Hanno Description: The majority of In this work, we introduce a new technique that combines two popular methods to estimate CVPR 2023: Gradient-based Uncertainty Attribution For Explainable Bayesian Deep Learning

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CVPR 2023: Gradient-based Uncertainty Attribution For Explainable Bayesian Deep Learning View more information on the DOE CSGF Program at Brian Lockwood University of Wyoming With the ...

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  • Despite tremendous success of modern neural networks, they are known to be overconfident even when the model encounters ...
  • CVPR 2023: Gradient-based Uncertainty Attribution For Explainable Bayesian Deep Learning
  • View more information on the DOE CSGF Program at Brian Lockwood University of Wyoming With the ...
  • In this work, we introduce a new technique that combines two popular methods to estimate
  • Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection, CoRL2022
  • Authors: Riedlinger, Tobias*; Rottmann, Matthias; Schubert, Marius; Gottschalk, Hanno Description: The majority of

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Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors
Quantification of Epistemic Uncertainty on Ground Motion Models
BayesOD A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors
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[ICIP2020] Gradients as a Measure of Uncertainty in Neural Networks
An Uncertainty Estimation Framework for Probabilistic Object Detection
DOE CSGF 2012: Gradient-based Methods for Rapid Uncertainty Quantification in Hypersonic Flows
Object Detection as Probabilistic Set Prediction [ECCV2022]
CVPR 2023: Gradient-based Uncertainty Attribution For Explainable Bayesian Deep Learning
Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection, CoRL2022
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Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Authors: Riedlinger, Tobias*; Rottmann, Matthias; Schubert, Marius; Gottschalk, Hanno Description: The majority of

Quantification of Epistemic Uncertainty on Ground Motion Models

Quantification of Epistemic Uncertainty on Ground Motion Models

Read more details and related context about Quantification of Epistemic Uncertainty on Ground Motion Models.

BayesOD A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

BayesOD A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

BayesOD A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

HEC WAT Epistemic and Aleatory Uncertainty

HEC WAT Epistemic and Aleatory Uncertainty

Read more details and related context about HEC WAT Epistemic and Aleatory Uncertainty.

[ICIP2020] Gradients as a Measure of Uncertainty in Neural Networks

[ICIP2020] Gradients as a Measure of Uncertainty in Neural Networks

Despite tremendous success of modern neural networks, they are known to be overconfident even when the model encounters ...

An Uncertainty Estimation Framework for Probabilistic Object Detection

An Uncertainty Estimation Framework for Probabilistic Object Detection

In this work, we introduce a new technique that combines two popular methods to estimate

DOE CSGF 2012: Gradient-based Methods for Rapid Uncertainty Quantification in Hypersonic Flows

DOE CSGF 2012: Gradient-based Methods for Rapid Uncertainty Quantification in Hypersonic Flows

View more information on the DOE CSGF Program at Brian Lockwood University of Wyoming With the ...

Object Detection as Probabilistic Set Prediction [ECCV2022]

Object Detection as Probabilistic Set Prediction [ECCV2022]

Read more details and related context about Object Detection as Probabilistic Set Prediction [ECCV2022].

CVPR 2023: Gradient-based Uncertainty Attribution For Explainable Bayesian Deep Learning

CVPR 2023: Gradient-based Uncertainty Attribution For Explainable Bayesian Deep Learning

CVPR 2023: Gradient-based Uncertainty Attribution For Explainable Bayesian Deep Learning

Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection, CoRL2022

Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection, CoRL2022

Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection, CoRL2022