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

格言

黃瓜

ABYSS

§?17?Verbraucherdarlehensvertragr the system. In particular, we show how a combination of methods can be used to estimate the overall machine learning performance, as well as to evaluate and reduce the impact of ML-specific insufficiencies, both during design and operation.

Diuretic

Uncertainty Quantification for Object Detection: Output- and Gradient-Based Approachesfor localization of uncertainty within the network architecture. We show that both sources of uncertainty are mutually non-redundant and can be combined beneficially. Furthermore, we show direct applications of uncertainty quantification by improving detection accuracy.

吸引人的花招

Evaluating Mixture-of-Experts Architectures for Network Aggregation baseline performance and also outperforms a simple aggregation via ensembling. A further advantage of an MoE is the increased interpretability—a comparison of pixel-wise predictions of the whole MoE model and the participating experts’ help to identify regions of high uncertainty in an input.

載貨清單

abreast

逃避現(xiàn)實

LARK

顛簸下上

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malic