Vahdat Abdelzad | DeepAI

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Featured Co-authors

Krzysztof Czarnecki
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Chengjie Huang
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Ashish Gaurav
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∙ 10/20/2022

XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection

Explainable AI (XAI) methods are frequently applied to obtain qualitativ...

0 Sunsheng Gu, et al. ∙

∙ 09/28/2022

Out-of-Distribution Detection for LiDAR-based 3D Object Detection

3D object detection is an essential part of automated driving, and deep ...

18 Chengjie Huang, et al. ∙

∙ 06/01/2022

LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection

The estimation of uncertainty in robotic vision, such as 3D object detec...

15 Matthew Pitropov, et al. ∙

∙ 08/30/2021

The missing link: Developing a safety case for perception components in automated driving

Safety assurance is a central concern for the development and societal a...

5 Rick Salay, et al. ∙

∙ 06/25/2020

The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches

Deep neural networks (DNNs) have become the de facto learning mechanism ...

35 Vahdat Abdelzad, et al. ∙

∙ 10/23/2019

Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output

Deep neural networks achieve superior performance in challenging tasks s...

0 Vahdat Abdelzad, et al. ∙

∙ 10/09/2019

Out-of-distribution Detection in Classifiers via Generation

By design, discriminatively trained neural network classifiers produce r...

0 Sachin Vernekar, et al. ∙

∙ 04/27/2019

Analysis of Confident-Classifiers for Out-of-distribution Detection

Discriminatively trained neural classifiers can be trusted, only when th...

0 Sachin Vernekar, et al. ∙

∙ 12/06/2018

Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance

There is an increasingly apparent need for validating the classification...

0 Taylor Denouden, et al. ∙

∙ 11/27/2018

Calibrating Uncertainties in Object Localization Task

In many safety-critical applications such as autonomous driving and surg...

0 Buu Phan, et al. ∙

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