Monte Carlo Stochastic Depth provides a theoretically linked and empirically competitive method for uncertainty quantification in modern deep learning models such as object detectors.
Laplace approximation based epis- temic uncertainty estimation in 3d object detection
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Monte Carlo Stochastic Depth for Uncertainty Estimation in Deep Learning
Monte Carlo Stochastic Depth provides a theoretically linked and empirically competitive method for uncertainty quantification in modern deep learning models such as object detectors.