arXiv:2605.28021v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs may deviate from the training distribution and overconfident predictions on unknown samples can lead to unreliable decisions. Outlier Exposure (OE) has emerged as a promising OOD detection paradigm by introducing auxiliary outliers during training to enlarge the margin between in-distribution (ID) and OOD samples. Existing OE-based methods typically enlarge this margin by employing uniform labels to
The paper addresses a critical, ongoing challenge in deploying AI in real-world, safety-critical environments where unknown inputs are common, building directly on the established 'Outlier Exposure' paradigm.
Improved OOD detection makes AI models more reliable and trustworthy, accelerating their adoption in high-stakes applications and reducing risks associated with overconfident predictions on novel data.
The proposed 'AOE' method offers a more robust and exhaustive approach to identifying out-of-distribution samples, improving the safety and predictability of ML deployments.
- · AI developers
- · Safety-critical AI applications
- · Machine learning researchers
- · Autonomous systems
- · Developers of less robust OOD methods
- · Industries reliant on unreliable AI
Increased reliability of AI models in diverse, real-world conditions.
Faster and safer integration of AI into regulated industries like healthcare and autonomous vehicles.
Enhanced public trust in AI systems leading to broader societal adoption and new economic opportunities.
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Read at arXiv cs.LG