arXiv:2503.05169v2 Announce Type: replace Abstract: Applying machine learning to increasingly high-dimensional problems with sparse or biased training data increases the risk that a model is used on inputs outside its training domain. For such out-of-distribution (OOD) inputs, the model can no longer make valid predictions, and its error is potentially unbounded. Since testing OOD detection methods on real-world datasets is complicated, we design a benchmark for OOD detection, which includes three novel and easily-visualisable toy examples. These simple examples provide direct and intuitive in
The increasing deployment of machine learning in critical, high-dimensional applications necessitates robust methods for identifying out-of-distribution inputs to ensure reliability and safety.
Improving OOD detection is crucial for the safe and reliable deployment of AI models across many sectors, especially as AI systems take on more critical decision-making roles.
The introduction of visual benchmarks and improved detectors for out-of-distribution inputs enhances the trustworthiness and applicability of AI systems in real-world scenarios.
- · AI developers
- · High-stakes AI applications (e.g., healthcare, autonomous vehicles)
- · AI safety researchers
- · Industries adopting AI
- · AI systems lacking robust OOD detection
- · Users vulnerable to unexpected AI failures
Wider adoption of OOD detection techniques in commercial AI products.
Increased user trust in AI systems due to improved reliability and explainable failures.
Accelerated integration of AI into regulated industries requiring high safety and transparency standards.
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Read at arXiv cs.LG