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.