Hyperbolic Metric Learning for Visual Outlier Detection
Alvaro Gonzalez-Jimenez Simone Lionetti Dena Bazazian Philippe Gottfrois Fabian Gröger Marc Pouly Alexander Navarini
Out-Of-Distribution (OOD) detection is critical to deploy deep learning
models in safety-critical applications. However, the inherent hierarchical
concept structure of visual data, which is instrumental to OOD detection, is
often poorly captured by conventional methods based on Euclidean geometry. This
work proposes a metric framework that leverages the strengths of Hyperbolic
geometry for OOD detection. Inspired by previous works that refine the decision
boundary for OOD data with synthetic outliers, we extend this method to
Hyperbolic space. Interestingly, we find that synthetic outliers do not benefit
OOD detection in Hyperbolic space as they do in Euclidean space. Furthermore we
explore the relationship between OOD detection performance and Hyperbolic
embedding dimension, addressing practical concerns in resource-constrained
environments. Extensive experiments show that our framework improves the FPR95
for OOD detection from 22\% to 15\% and from 49% to 28% on CIFAR-10 and
CIFAR-100 respectively compared to Euclidean methods.