Deep Neural Networks and Data for Automated Driving : Robustness, Uncertainty Quantification, and Insights Towards Safety — 1st ed. 2022 (English)
- New search for: Gottschalk, Hanno
- New search for: Houben, Sebastian
- New search for: Fingscheidt, Tim
2022
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ISBN:
- Book / Electronic Resource
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Title:Deep Neural Networks and Data for Automated Driving : Robustness, Uncertainty Quantification, and Insights Towards Safety
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Contributors:
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Edition:1st ed. 2022
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Publisher:
- New search for: Imprint: Springer
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Place of publication:Cham
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Publication date:2022
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Size:1 Online-Ressource (XVIII, 427 p. 117 illus., 103 illus. in color)
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ISBN:
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DOI:
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Type of media:Book
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Type of material:Electronic Resource
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Language:English
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Keywords:
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Source:
Table of contents eBook
The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.
- 1
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Inspect, Understand, Overcome: A Survey of Practical Methods for AI SafetyHouben, Sebastian / Abrecht, Stephanie / Akila, Maram / Bär, Andreas / Brockherde, Felix / Feifel, Patrick / Fingscheidt, Tim / Gannamaneni, Sujan Sai / Ghobadi, Seyed Eghbal / Hammam, Ahmed et al. | 2022
- 2
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Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?Gottschalk, Hanno / Rottmann, Matthias / Saltagic, Maida et al. | 2022
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Analysis and Comparison of Datasets by Leveraging Data Distributions in Latent SpacesStage, Hanno / Ries, Lennart / Langner, Jacob / Otten, Stefan / Sax, Eric et al. | 2022
- 4
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Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact SimulationHagn, Korbinian / Grau, Oliver et al. | 2022
- 5
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Improved DNN Robustness by Multi-task Training with an Auxiliary Self-Supervised TaskKlingner, Marvin / Fingscheidt, Tim et al. | 2022
- 6
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Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and SegmentationHashemi, Atiye Sadat / Bär, Andreas / Mozaffari, Saeed / Fingscheidt, Tim et al. | 2022
- 7
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Invertible Neural Networks for Understanding Semantics of Invariances of CNN RepresentationsRombach, Robin / Esser, Patrick / Blattmann, Andreas / Ommer, Björn et al. | 2022
- 8
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Confidence Calibration for Object Detection and SegmentationKüppers, Fabian / Haselhoff, Anselm / Kronenberger, Jan / Schneider, Jonas et al. | 2022
- 9
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Uncertainty Quantification for Object Detection: Output- and Gradient-Based ApproachesRiedlinger, Tobias / Schubert, Marius / Kahl, Karsten / Rottmann, Matthias et al. | 2022
- 10
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Detecting and Learning the Unknown in Semantic SegmentationChan, Robin / Uhlemeyer, Svenja / Rottmann, Matthias / Gottschalk, Hanno et al. | 2022
- 11
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Evaluating Mixture-of-Experts Architectures for Network AggregationPavlitskaya, Svetlana / Hubschneider, Christian / Weber, Michael et al. | 2022
- 12
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Safety Assurance of Machine Learning for Perception FunctionsBurton, Simon / Hellert, Christian / Hüger, Fabian / Mock, Michael / Rohatschek, Andreas et al. | 2022
- 13
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A Variational Deep Synthesis Approach for Perception ValidationGrau, Oliver / Hagn, Korbinian / Syed Sha, Qutub et al. | 2022
- 14
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The Good and the Bad: Using Neuron Coverage as a DNN Validation TechniqueGannamaneni, Sujan Sai / Akila, Maram / Heinzemann, Christian / Woehrle, Matthias et al. | 2022
- 15
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Joint Optimization for DNN Model Compression and Corruption RobustnessVarghese, Serin / Hümmer, Christoph / Bär, Andreas / Hüger, Fabian / Fingscheidt, Tim et al. | 2022