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cs.CV2017★ 499 cited
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Alex Kendall, Yarin Gal, Roberto Cipolla
Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives. In this paper we make the observation that the performa…
cs.CV2017★ 2.9k cited
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Alex Kendall, Yarin Gal
There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for unc…