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Deep Neural Networks and Data for Automated Driving

- Robustness, Uncertainty Quantification, and Insights Towards Safety
Engelsk Hardback

Deep Neural Networks and Data for Automated Driving

- Robustness, Uncertainty Quantification, and Insights Towards Safety
Engelsk Hardback
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Chapter 1. Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety.- Chapter 2. Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?.- Chapter 3. Analysis and Comparison of Datasets by Leveraging Data Distributions in Latent Spaces.- Chapter 4. Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation.- Chapter 5. Improved DNN Robustness by Multi-Task Training With an Auxiliary Self-Supervised Task.- Chapter 6. Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation.- Chapter 7. Invertible Neural Networks for Understanding Semantics of Invariances of CNN Representations.- Chapter 8. Confidence Calibration for Object Detection and Segmentation.- Chapter 9. Uncertainty Quantification for Object Detection: Output- and Gradient-based Approaches.- Chapter 10. Detecting and Learning the Unknown in Semantic Segmentation.- Chapter 11. Evaluating Mixture-of-Expert Architectures for Network Aggregation.- Chapter 12. Safety Assurance of Machine Learning for Perception Functions.- Chapter 13. A Variational Deep Synthesis Approach for Perception Validation.- Chapter 14. The Good and the Bad: Using Neuron Coverage as a DNN Validation Technique.- Chapter 15. Joint Optimization for DNN Model Compression and Corruption Robustness.
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Chapter 1. Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety.- Chapter 2. Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?.- Chapter 3. Analysis and Comparison of Datasets by Leveraging Data Distributions in Latent Spaces.- Chapter 4. Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation.- Chapter 5. Improved DNN Robustness by Multi-Task Training With an Auxiliary Self-Supervised Task.- Chapter 6. Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation.- Chapter 7. Invertible Neural Networks for Understanding Semantics of Invariances of CNN Representations.- Chapter 8. Confidence Calibration for Object Detection and Segmentation.- Chapter 9. Uncertainty Quantification for Object Detection: Output- and Gradient-based Approaches.- Chapter 10. Detecting and Learning the Unknown in Semantic Segmentation.- Chapter 11. Evaluating Mixture-of-Expert Architectures for Network Aggregation.- Chapter 12. Safety Assurance of Machine Learning for Perception Functions.- Chapter 13. A Variational Deep Synthesis Approach for Perception Validation.- Chapter 14. The Good and the Bad: Using Neuron Coverage as a DNN Validation Technique.- Chapter 15. Joint Optimization for DNN Model Compression and Corruption Robustness.
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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 427
ISBN-13: 9783031012327
Indbinding: Hardback
Udgave:
ISBN-10: 3031012321
Kategori: Matematisk modellering
Udg. Dato: 18 jun 2022
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer International Publishing AG
Oplagsdato: 18 jun 2022
Forfatter(e):
Forfatter(e)

Kategori Matematisk modellering

ISBN-13 9783031012327

Sprog Engelsk

Indbinding Hardback

Sider 427

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 18 jun 2022

Oplagsdato 18 jun 2022

Forlag Springer International Publishing AG