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[R] Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision

We propose an evaluation framework for predictive uncertainty estimation that is specifically designed to test the robustness required in real-world computer vision applications. Using the proposed framework, we perform an extensive comparison of the popular ensembling and MC-dropout methods on the tasks of depth completion and street-scene semantic segmentation. Our comparison suggests that ensembling consistently provides more reliable uncertainty estimates.

arXiv: https://arxiv.org/abs/1906.01620

Code: https://github.com/fregu856/evaluating_bdl

Video: https://youtu.be/CabPVqtzsOI

Project page: http://www.fregu856.com/publication/evaluating_bdl/

submitted by /u/dirac-hatt
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.