November 2019
tl;dr: Proof of concept by applying Uncertainty calibration to object detector.
For a more theoretical treatment refer to accurate uncertainty via calibrated regression. A more detailed application is can we trust you.
- Validating uncertainty estimates: plot regressed aleatoric uncertainty
$\sigma_i^2$ and$(b_i - \bar{b_i})^2$ - To find 90% confidence interval, the upper and lower bounds are given by
$\hat{P^{-1}}(r \pm 90/2)$ , where$r = \hat{P(x)}$ and$\hat{P}$ is the P after calibration.
- Summary of technical details
- Questions and notes on how to improve/revise the current work