TY  - GEN
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
AV  - public
SP  - 415
Y1  - 2021/01/01/
EP  - 427
TI  - Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation
T3  - Lecture Notes in Computer Science
A1  - Popescu, SG
A1  - Sharp, DJ
A1  - Cole, JH
A1  - Kamnitsas, K
A1  - Glocker, B
CY  - Cham, Switzerland
UR  - https://doi.org/10.1007/978-3-030-78191-0_32
PB  - Springer
SN  - 1611-3349
N2  - 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 never 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.
ID  - discovery10134583
ER  -