Being a Bit Frequentist Improves Bayesian Neural Networks

• URL: http://arxiv.org/abs/2106.10065v1
• Date: Fri, 18 Jun 2021 11:22:42 GMT
• Title: Being a Bit Frequentist Improves Bayesian Neural Networks
• Authors: Agustinus Kristiadi and Matthias Hein and Philipp Hennig
• Abstract summary: We show that OOD-trained BNNs are competitive to, if not better than recent frequentist baselines. This work provides strong baselines for future work in both Bayesian and frequentist UQ.
• Score: 76.73339435080446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite their compelling theoretical properties, Bayesian neural networks (BNNs) tend to perform worse than frequentist methods in classification-based uncertainty quantification (UQ) tasks such as out-of-distribution (OOD) detection and dataset-shift robustness. In this work, based on empirical findings in prior works, we hypothesize that this issue is due to the avoidance of Bayesian methods in the so-called "OOD training" -- a family of techniques for incorporating OOD data during training process, which has since been an integral part of state-of-the-art frequentist UQ methods. To validate this, we treat OOD data as a first-class citizen in BNN training by exploring four different ways of incorporating OOD data in Bayesian inference. We show in extensive experiments that OOD-trained BNNs are competitive to, if not better than recent frequentist baselines. This work thus provides strong baselines for future work in both Bayesian and frequentist UQ.

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