Diversity As a Paradigm for Out-of-Distribution Detection

Out-of-distribution (OOD) detection is critical for the safe deployment of machine learning systems and for scientific discovery. Existing post-hoc detectors typically rely on model confidence scores or likelihood estimates in feature space, often under restrictive distributional assumptions. In this work, we introduce a new paradigm and formulate OOD detection from a diversity perspective. We propose Vendi Score Out-Of-Distribution detection (VSOOD), an OOD detector based on the Vendi Scores (VS), a family of similarity-based diversity metrics. VSOOD quantifies how much a test sample increases the VS of the in-distribution feature set, providing a principled notion of novelty that does not require density modeling. VSOOD is linear-time and naturally combines class-conditional (local) and dataset-level (global) novelty signals. Across multiple image classification benchmarks and network architectures, VSOOD achieves state-of-the-art OOD detection performance. Remarkably, VSOOD retains this performance when computed using only 1% of the training data, enabling deployment in memory- or access-constrained settings.