mlsanityData to 7 Oct 2026

Hyperbolic Metric Learning for Visual Outlier Detection

arXiv 2403.15260 ↗First posted 22 Mar 2024cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 66 of its 82 references that are in the map. First posted on arXiv 22 Mar 2024.

Builds on

66 of 82 references.

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Is Hyperbolic Space All You Need for Medical Anomaly Detection?cited by 5
  2. 2025Geometry-aware Distance Measure for Diverse Hierarchical Structures in Hyperbolic Spacescited by 2
  3. 2026Head Similarity: Modeling Structured Whole-Head Appearance Beyond Face Recognitioncited by 1