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

近地點(diǎn)

書目名稱Deep Neural Networks and Data for Automated Driving影響因子(影響力)




書目名稱Deep Neural Networks and Data for Automated Driving影響因子(影響力)學(xué)科排名




書目名稱Deep Neural Networks and Data for Automated Driving網(wǎng)絡(luò)公開度




書目名稱Deep Neural Networks and Data for Automated Driving網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Deep Neural Networks and Data for Automated Driving被引頻次




書目名稱Deep Neural Networks and Data for Automated Driving被引頻次學(xué)科排名




書目名稱Deep Neural Networks and Data for Automated Driving年度引用




書目名稱Deep Neural Networks and Data for Automated Driving年度引用學(xué)科排名




書目名稱Deep Neural Networks and Data for Automated Driving讀者反饋




書目名稱Deep Neural Networks and Data for Automated Driving讀者反饋學(xué)科排名

HAWK

http://image.papertrans.cn/d/image/264648.jpg

allergy

aneurysm

Cirrhosis

Deutsches Verfassungsrecht 1806 - 1918d robust deep neural network (DNN) functions requires new validation methods. A core insufficiency of DNNs is the lack of generalization for out-of-distribution datasets. One path to overcome this insufficiency is through the analysis and comparison of the domains of training and test datasets. This

牽連

Deutsches Verfassungsrecht 1806 - 1918its ability to simulate rare cases, avoidance of privacy issues, and generation of pixel-accurate ground truth data. Today, physical-based rendering (PBR) engines simulate already a wealth of realistic optical effects but are mainly focused on the human perception system. Whereas the perceptive func

牽連

Deutsches Verfassungsrecht 1806 - 1918red images, their robustness under real conditions, i.e., on images being perturbed with noise patterns or adversarial attacks, is often subject to a significantly decreased performance. In this chapter, we address this problem for the task of semantic segmentation by proposing multi-task training w

Accord

https://doi.org/10.1007/978-3-662-64750-9 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

大吃大喝

Deutsches Verfassungsrecht 1806 - 1918resentations 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

pulmonary-edema

Deutsches Verfassungsrecht 1806 - 1918 medical image diagnosis. However, although the task of confidence calibration has been investigated on classification problems, thorough investigations on object detection and segmentation problems are still missing. Therefore, we focus on the investigation of confidence calibration for object dete