Google Research

Deep Classifiers with Label Noise Modeling and Distance Awareness

NeurIPS 2021 Workshop on Bayesian Deep Learning (2021) (to appear)

Abstract

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.

Research Areas

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work