Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributional Gaussian Process Layers for Outlier Detection in Image
  Segmentation   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.

26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess