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September 27, 2025Open Access

OOD Detectors Are Best Used Runtime Verifiers, Not Semantic Shift Classifiers

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Authors

BTBirk Torpmann-HagenPHPål HalvorsenMRMichael A. Riegler

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Overview

Position paper reveals how OOD detectors function as runtime verifiers, suggesting enhanced role in managing semantic shifts.

Key Points

  • Implementing OOD detectors as runtime verifiers reduces expected costs per patient by over 40%.
  • OOD detectors measure data support in the training distribution to improve neural network accuracy.
  • The shift in focus of OOD detection research is crucial for responsible deployment in high-stakes applications.
  • Empirical analysis conducted in a polyp segmentation case study supports the benefits of using OOD detectors.

Cite This Study

Torpmann-Hagen et al. (2025) studied this question.

synapsesocial.com/papers/68d7be62eebfec0fc5237a3ehttps://doi.org/10.21203/rs.3.rs-7696794/v1
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1General OOD detection via model-aware and subspace-aware variable priority2026
  2. 2Continual Unsupervised Out-of-Distribution Detection2024 · 1 citations
  3. 3A Noisy Elephant in the Room: Is Your out-of-Distribution Detector Robust to Label Noise?2024 · 1 citations
  4. 4A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?2024
  5. 5Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors2024