Uncertainty of Network Topology with Applications to Out-of-Distribution Detection

TL;DR

This paper introduces a novel topological uncertainty measure called pTU for Bayesian neural networks, which aids in out-of-distribution detection by assessing input-model interactions and is architecture-insensitive.

Contribution

The paper proposes the predictive topological uncertainty (pTU) as a new topological summary for Bayesian neural networks, specifically designed for OOD detection and insensitive to model architecture.

Findings

pTU effectively detects OOD inputs with high statistical power.

The method is robust and sensitive across various experimental settings.

pTU provides model-agnostic uncertainty quantification.

Abstract

Persistent homology (PH) is a crucial concept in computational topology, providing a multiscale topological description of a space. It is particularly significant in topological data analysis, which aims to make statistical inference from a topological perspective. In this work, we introduce a new topological summary for Bayesian neural networks, termed the predictive topological uncertainty (pTU). The proposed pTU measures the uncertainty in the interaction between the model and the inputs. It provides insights from the model perspective: if two samples interact with a model in a similar way, then they are considered identically distributed. We also show that the pTU is insensitive to the model architecture. As an application, pTU is used to solve the out-of-distribution (OOD) detection problem, which is critical to ensure model reliability. Failure to detect OOD input can lead to incorrect and unreliable predictions. To address this issue, we propose a significance test for OOD based on the pTU, providing a statistical framework for this issue. The effectiveness of the framework is validated through various experiments, in terms of its statistical power, sensitivity, and robustness.