The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 721–728, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-721-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 721–728, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-721-2022
 
30 May 2022
30 May 2022

COMPARISON OF UNCERTAINTY QUANTIFICATION METHODS FOR CNN-BASED REGRESSION

K. Wursthorn, M. Hillemann, and M. Ulrich K. Wursthorn et al.
  • Karlsruhe Institute of Technology (KIT), Institute of Photogrammetry and Remote Sensing (IPF), Karlsruhe, Germany

Keywords: Deep Learning, Regression, Convolutional Neural Network, Uncertainty Quantification, Bayesian Modelling, Variational Inference

Abstract. The evaluation of reliability is not only of high importance for safety-critical deep learning applications but for object pose estimation as well. The uncertainty of the result is one way to express its reliability. In order to better understand existing uncertainty quantification (UQ) methods and their performance on image-based regression tasks, we use a small CNN and various scenarios to evaluate the estimated uncertainties. The evaluation is done on different simplistic synthetic datasets, consisting of gray-scale images of squares on a darker background. We train the CNN to predict the square center position of the square in the image. We compare how different UQ methods perform under dataset shift, rotation, occlusion, noise changes in the images.