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arXiv · 2006.11590

Regression Prior Networks

Andrey Malinin·Sergey Chervontsev·Ivan Provilkov·Mark Gales
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Abstract

Prior Networks are a recently developed class of models which yield interpretable measures of uncertainty and have been shown to outperform state-of-the-art ensemble approaches on a range of tasks. They can also be used to distill an ensemble of models via Ensemble Distribution Distillation (EnD$^2$), such that its accuracy, calibration and uncertainty estimates are retained within a single model. However, Prior Networks have so far been developed only for classification tasks. This work extends Prior Networks and EnD$^2$ to regression tasks by considering the Normal-Wishart distribution. The properties of Regression Prior Networks are demonstrated on synthetic data, selected UCI datasets and a monocular depth estimation task, where they yield performance competitive with ensemble approaches.

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Andrey Malinin, Sergey Chervontsev, Ivan Provilkov, Mark Gales. 2020-06-20. Regression Prior Networks. https://arxiv.org/abs/2006.11590

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