arXiv:2402.17888v5 Announce Type: replace Abstract: Post-hoc out-of-distribution (OOD) detection has garnered intensive attention in reliable machine learning. Many efforts have been dedicated to deriving score functions based on logits, distances, or rigorous data distribution assumptions to identify low-scoring OOD samples. Nevertheless, these estimate scores may fail to accurately reflect the true data density or impose impractical constraints. To provide a unified perspective on density-based score design, we propose a novel theoretical framework grounded in Bregman divergence, which exten
The increasing deployment of AI in critical applications necessitates robust methods for identifying out-of-distribution data to ensure reliability and safety.
Improved OOD detection makes AI systems more reliable and trustworthy, which is crucial for their integration into sensitive and autonomous environments.
The ability to accurately reflect true data density for OOD detection with a tractable framework changes how AI systems can be designed for safety and robustness.
- · AI developers
- · Reliable machine learning platforms
- · Industries relying on safe AI deployment
- · Systems with poor OOD detection capabilities
- · AI applications in safety-critical domains
More secure and reliable AI deployments in real-world applications.
Accelerated adoption of AI in sectors requiring high assurance, like autonomous vehicles and medical diagnostics.
Shift towards standardized OOD detection benchmarks impacting regulatory frameworks for AI safety.
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