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

情感

Invertible Neural Networks for Understanding Semantics of Invariances of CNN Representationsns to (i) expose their semantic meaning, (ii) semantically modify a representation, and (iii) visualize individual learned semantic concepts and invariances. Our invertible approach significantly extends the abilities to understand black-box models by enabling post hoc interpretations of state-of-th

Femish

Confidence Calibration for Object Detection and Segmentationion of object detection and segmentation models. We examine several network architectures on MS COCO as well as on Cityscapes and show that especially object detection as well as instance segmentation models are intrinsically miscalibrated given the introduced definition of calibration. Using our pr

克制

决定性

A Variational Deep Synthesis Approach for Perception Validationnd combined with our variational approach we can effectively simulate and test a wide range of additional conditions as, e.g., various illuminations. We can demonstrate that our generative approach produces a better approximation of the spatial object distribution to real datasets, compared to hand-

挫败

Joint Optimization for DNN Model Compression and Corruption Robustnesstness of the . network by 8.39% absolute mean performance under corruption (mPC) on the Cityscapes dataset, and by 2.93% absolute mPC on the Sim KI-A dataset, while generalizing even to augmentations not seen by the network in the training process. This is achieved with only minor degradations on un

蚊子

https://doi.org/10.1007/978-3-662-39531-8encies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent

Radiculopathy

公社

Ibd810