Don't Collapse Your Features: Why CenterLoss Hurts OOD Detection and Multi-Scale Mahalanobis Wins
arXiv:2605.21493v1 Announce Type: new Abstract: The ability to detect out-of-distribution (OOD) inputs is fundamental to safe deployment of machine learning systems. Yet, current methods often rely on feature representations that are optimised solely for classification accuracy, neglecting the distinct requirements of epistemic uncertainty. We introduce GOEN (Geometry-Optimised Epistemic Network), a simple pipeline that combines multi-scale features, L2 normalisation, Mahalanobis distance, and a calibration head trained with real hard OOD examples. Through systematic ablation we uncover a coun
The increasing deployment of machine learning systems in critical applications necessitates robust methods for detecting out-of-distribution inputs, enhancing safety and reliability.
This development addresses a fundamental limitation in current AI systems, improving their ability to handle novel or unexpected data, crucial for dependable AI deployment across industries.
Machine learning models can now be designed with improved epistemic uncertainty detection, leading to safer and more robust AI applications that are less prone to catastrophic failures on unseen data.
- · AI safety researchers
- · Autonomous system developers
- · High-stakes AI industries
- · Machine learning platform providers
- · Organizations deploying unreliable AI
- · Developers neglecting OOD detection
Improved OOD detection leads to more trustworthy and deployable AI systems.
Increased public and regulatory confidence in AI adoption, particularly in sensitive sectors.
New AI safety standards and benchmarks emerge, pushing the industry towards more robust designs.
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Read at arXiv cs.LG