110 citations · 176 across the 4 of their papers we have counts for
5 papers
Confidence Calibration for Object Detection and Segmentation
Fabian Küppers, Anselm Haselhoff, Jan Kronenberger +1
Calibrated confidence estimates obtained from neural networks are crucial, particularly for safety-critical applications such as autonomous driving or medical image diagnosis. Howe…
Bayesian Confidence Calibration for Epistemic Uncertainty Modelling
Fabian Küppers, Jan Kronenberger, Jonas Schneider +1
Modern neural networks have found to be miscalibrated in terms of confidence calibration, i.e., their predicted confidence scores do not reflect the observed accuracy or precision.…
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Sebastian Houben, Stephanie Abrecht, Maram Akila +38
The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These s…
Dependency Decomposition and a Reject Option for Explainable Models
Jan Kronenberger, Anselm Haselhoff
Deploying machine learning models in safety-related do-mains (e.g. autonomous driving, medical diagnosis) demands for approaches that are explainable, robust against adversarial at…
Multivariate Confidence Calibration for Object Detection
Fabian Küppers, Jan Kronenberger, Amirhossein Shantia +1
Unbiased confidence estimates of neural networks are crucial especially for safety-critical applications. Many methods have been developed to calibrate biased confidence estimates.…