Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Stifter, Thomas"'
When Deep Neural Networks (DNNs) are used in safety-critical systems, engineers should determine the safety risks associated with failures (i.e., erroneous outputs) observed during testing. For DNNs processing images, engineers visually inspect all f
Externí odkaz:
http://arxiv.org/abs/2204.00480
Learning on synthetic data and transferring the resulting properties to their real counterparts is an important challenge for reducing costs and increasing safety in machine learning. In this work, we focus on autoencoder architectures and aim at lea
Externí odkaz:
http://arxiv.org/abs/2204.00386
The reliability assessment of a machine learning model's prediction is an important quantity for the deployment in safety critical applications. Not only can it be used to detect novel sceneries, either as out-of-distribution or anomaly sample, but i
Externí odkaz:
http://arxiv.org/abs/2204.00382
Common domain shift problem formulations consider the integration of multiple source domains, or the target domain during training. Regarding the generalization of machine learning models between different car interiors, we formulate the criterion of
Externí odkaz:
http://arxiv.org/abs/2105.03164
Automatically detecting the positions of key-points (e.g., facial key-points or finger key-points) in an image is an essential problem in many applications, such as driver's gaze detection and drowsiness detection in automated driving systems. With t
Externí odkaz:
http://arxiv.org/abs/2012.06511
Images recorded during the lifetime of computer vision based systems undergo a wide range of illumination and environmental conditions affecting the reliability of previously trained machine learning models. Image normalization is hence a valuable pr
Externí odkaz:
http://arxiv.org/abs/2011.03428
Autor:
Da Cruz, Steve Dias, Wasenmüller, Oliver, Beise, Hans-Peter, Stifter, Thomas, Stricker, Didier
We release SVIRO, a synthetic dataset for sceneries in the passenger compartment of ten different vehicles, in order to analyze machine learning-based approaches for their generalization capacities and reliability when trained on a limited number of
Externí odkaz:
http://arxiv.org/abs/2001.03483
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
In Colloids and Surfaces A: Physicochemical and Engineering Aspects 1999 154(1):65-73
