Qualitative results for the paper:Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision, 2019.Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön.arXiv: https://arxiv.org/abs/1906.01620Code: https://github.com/fregu856/evaluatin...Project page: http://www.fregu856.com/publication/e...We propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning. It is specifically designed to test the robustness required in real-world computer vision applications. We also apply our proposed framework to provide the first properly extensive and conclusive comparison of the two current state-of-the-art scalable methods: ensembling and MC-dropout. Our comparison demonstrates that ensembling consistently provides more reliable and practically useful uncertainty estimates.- All shown results are for ensembling with M = 8.- Street-scene semantic segmentation: 0:00 - 8:22.- - Cityscapes to Cityscapes (real to real): 0:00.- - Synscapes to Cityscapes (synthetic to real): 2:30.- - Synscapes to Synscapes (synthetic to synthetic): 5:00. - - Cityscapes to Synscapes (real to synthetic): 6:41- Depth completion: 8:22 - 14:18.- - virtual KITTI to virtual KITTI (synthetic to synthetic): 8:22.- - virtual KITTI to KITTI (synthetic to real): 9:26.- On Cityscapes, the input image, prediction and predictive entropy are visualized.- On Synscapes, the input image, ground truth, prediction and predictive entropy are visualized.- For depth completion, the input image, input sparse depth map, ground truth depth map, prediction, predictive uncertainty, aleatoric uncertainty and epistemic uncertainty are visualized.- Black: minimum uncertainty, white: maximum uncertainty.