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Deep Neural Networks and Data for Automated Driving

Overview of attention for book
Cover of 'Deep Neural Networks and Data for Automated Driving'

Table of Contents

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    Book Overview
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    Chapter 1 Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
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    Chapter 2 Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?
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    Chapter 3 Analysis and Comparison of Datasets by Leveraging Data Distributions in Latent Spaces
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    Chapter 4 Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation
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    Chapter 5 Improved DNN Robustness by Multi-task Training with an Auxiliary Self-Supervised Task
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    Chapter 6 Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation
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    Chapter 7 Invertible Neural Networks for Understanding Semantics of Invariances of CNN Representations
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    Chapter 8 Confidence Calibration for Object Detection and Segmentation
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    Chapter 9 Uncertainty Quantification for Object Detection: Output- and Gradient-Based Approaches
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    Chapter 10 Detecting and Learning the Unknown in Semantic Segmentation
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    Chapter 11 Evaluating Mixture-of-Experts Architectures for Network Aggregation
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    Chapter 12 Safety Assurance of Machine Learning for Perception Functions
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    Chapter 13 A Variational Deep Synthesis Approach for Perception Validation
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    Chapter 14 The Good and the Bad: Using Neuron Coverage as a DNN Validation Technique
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    Chapter 15 Joint Optimization for DNN Model Compression and Corruption Robustness
Overall attention for this book and its chapters
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Mentioned by

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Citations

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25 Dimensions

Readers on

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43 Mendeley
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Title
Deep Neural Networks and Data for Automated Driving
Published by
Springer International Publishing, July 2022
DOI 10.1007/978-3-031-01233-4
ISBNs
978-3-03-101232-7, 978-3-03-101233-4
Editors

Fingscheidt, Tim, Gottschalk, Hanno, Houben, Sebastian

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X Demographics

X Demographics

The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 14%
Student > Master 3 7%
Researcher 3 7%
Student > Postgraduate 2 5%
Student > Bachelor 2 5%
Other 1 2%
Unknown 26 60%
Readers by discipline Count As %
Engineering 8 19%
Computer Science 7 16%
Materials Science 1 2%
Unknown 27 63%

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