Distributional Gaussian Processes Layers for Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2206.13346v1
• Date: Mon, 27 Jun 2022 14:49:48 GMT
• Title: Distributional Gaussian Processes Layers for Out-of-Distribution Detection
• Authors: Sebastian G. Popescu, David J. Sharp, James H. Cole, Konstantinos Kamnitsas and Ben Glocker
• Abstract summary: It is unsure whether out-of-distribution detection models reliant on deep neural networks are suitable for detecting domain shifts in medical imaging. We propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space. Our uncertainty estimates result in out-of-distribution detection that outperforms the capabilities of previous Bayesian networks.
• Score: 18.05109901753853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning models deployed on medical imaging tasks must be equipped with out-of-distribution detection capabilities in order to avoid erroneous predictions. It is unsure whether out-of-distribution detection models reliant on deep neural networks are suitable for detecting domain shifts in medical imaging. Gaussian Processes can reliably separate in-distribution data points from out-of-distribution data points via their mathematical construction. Hence, we propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably propagate uncertainty. This directly replaces convolving Gaussian Processes with a distance-preserving affine operator on distributions. Our experiments on brain tissue-segmentation show that the resulting architecture approaches the performance of well-established deterministic segmentation algorithms (U-Net), which has not been achieved with previous hierarchical Gaussian Processes. Moreover, by applying the same segmentation model to out-of-distribution data (i.e., images with pathology such as brain tumors), we show that our uncertainty estimates result in out-of-distribution detection that outperforms the capabilities of previous Bayesian networks and reconstruction-based approaches that learn normative distributions. To facilitate future work our code is publicly available.

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