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cs.CV2024★ 1 cited
Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning
Nils Lehmann, Jakob Gawlikowski, Adam J. Stewart +4
Uncertainty quantification (UQ) is an essential tool for applying deep neural networks (DNNs) to real world tasks, as it attaches a degree of confidence to DNN outputs. However, de…
cs.CV2024
Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction
Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann +1
Quantifying a model's predictive uncertainty is essential for safety-critical applications such as autonomous driving. We consider quantifying such uncertainty for multi-object det…