Titlebook: Deep Neural Networks and Data for Automated Driving; Robustness, Uncertai Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben Book‘‘‘‘‘‘‘‘ 20

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Improving Transferability of?Generated Universal Adversarial Perturbations for?Image Classification they are vulnerable to adversarial perturbations. Recent works have proven the existence of universal adversarial perturbations (UAPs), which, when added to most images, destroy the output of the respective perception function. Existing attack methods often show a low success rate when attacking ta

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Invertible Neural Networks for Understanding Semantics of Invariances of CNN Representationsresentations and, particularly, the invariances they capture turn neural networks into black-box models that lack interpretability. To open such a black box, it is, therefore, crucial to uncover the different semantic concepts a model has learned as well as those that it has learned to be invariant

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Detecting and?Learning the?Unknown in?Semantic Segmentationy are usually trained on a closed set of object classes appearing in a closed operational domain. However, this is in contrast to the open world assumption in automated driving that DNNs are deployed to. Therefore, DNNs necessarily face data that they have never encountered previously, also known as

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Evaluating Mixture-of-Experts Architectures for Network Aggregationct the most suitable distribution of the expert’s outputs for each input. An MoE thus not only relies on redundancy for increased robustness—we also demonstrate how this architecture can provide additional interpretability, while retaining performance similar to a standalone network. As an example,