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Found 8 papers, 3 papers with code
Segmentation-guided Domain Adaptation for Efficient Depth Completion
Complete depth information and efficient estimators have become vital ingredients in scene understanding for automated driving tasks.
Parametric and Multivariate Uncertainty Calibration for Regression and Object Detection
Our experiments show that common detection models overestimate the spatial uncertainty in comparison to the observed error.
Confidence Calibration for Object Detection and Segmentation
Calibrated confidence estimates obtained from neural networks are crucial, particularly for safety-critical applications such as autonomous driving or medical image diagnosis.
Bayesian Confidence Calibration for Epistemic Uncertainty Modelling
We introduce Bayesian confidence calibration - a framework to obtain calibrated confidence estimates in conjunction with an uncertainty of the calibration method.
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods.
From Black-box to White-box: Examining Confidence Calibration under different Conditions
Based on previous work, we study the miscalibration of object detection models with respect to image location and box scale.
Dependency Decomposition and a Reject Option for Explainable Models
Deploying machine learning models in safety-related do-mains (e. g. autonomous driving, medical diagnosis) demands for approaches that are explainable, robust against adversarial attacks and aware of the model uncertainty.
Multivariate Confidence Calibration for Object Detection
Therefore, we present a novel framework to measure and calibrate biased (or miscalibrated) confidence estimates of object detection methods.
