3 citations · 4 across the 4 of their papers we have counts for
5 papers · 1 filter
The Gaussian Discriminant Variational Autoencoder (GdVAE): A Self-Explainable Model with Counterfactual Explanations
Anselm Haselhoff, Kevin Trelenberg, Fabian Küppers +1
Visual counterfactual explanation (CF) methods modify image concepts, e.g, shape, to change a prediction to a predefined outcome while closely resembling the original query image.…
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.…
From Black-box to White-box: Examining Confidence Calibration under different Conditions
Franziska Schwaiger, Maximilian Henne, Fabian Küppers +3
Confidence calibration is a major concern when applying artificial neural networks in safety-critical applications. Since most research in this area has focused on classification i…
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.…