ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection

Understand

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 extends distribution considerations to encompass an exponential family of distributions.