Out-of-Distribution Detection Threshold Modeling

Abstract

Out-of-Distribution (OOD) detection is essential for maintaining the reliability of machine learning
systems when deployed in dynamic real-world environments where input distributions may shift over
time. This study evaluates multiple threshold modeling approaches, including confidence-based
scoring, latent-space distance evaluation, energy-based scoring, and adaptive threshold recalibration.
Experimental results demonstrate that confidence-based thresholds are insufficient for distinguishing
unfamiliar samples due to poor uncertainty calibration. Distance-based and energy-based scoring
models provide more robust separation between in-distribution and OOD inputs by leveraging the
geometric structure of learned feature manifolds. Furthermore, adaptive thresholding strategies
maintain stable detection performance under distributional drift, outperforming fixed thresholds in
evolving operational contexts. These findings highlight the importance of geometry-aware and
dynamically tunable threshold models for reliable deployment of neural systems in production
settings.