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Deep Classifiers with Label Noise Modeling and Distance Awareness

Vincent Fortuin
Mark Patrick Collier
Florian Wenzel
James Urquhart Allingham
Jesse Berent
Rodolphe Jenatton
NeurIPS 2021 Workshop on Bayesian Deep Learning (2021) (to appear)


Uncertainty estimation in deep learning has recently emerged as a crucial area of interest to advance reliability and robustness of deep learning models, especially in safety-critical applications. While there have been many proposed methods that either focus on distance-aware model uncertainties for out-of-distribution detection or respectively on input-dependent label uncertainties for in-distribution calibration, combining these two approaches has been less well explored. In this work, we propose to combine these two ideas to achieve a joint modeling of model (epistemic) and data (aleatoric) uncertainty. We show that our combined model affords a favorable combination between these two complementary types of uncertainty and thus achieves good performance in-distribution and out-of-distribution on different benchmark datasets.

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