Vendi Novelty Scores for Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2602.10062v1
• Date: Tue, 10 Feb 2026 18:30:29 GMT
• Title: Vendi Novelty Scores for Out-of-Distribution Detection
• Authors: Amey P. Pasarkar, Adji Bousso Dieng,
• Abstract summary: Vendi Novelty Score (VNS) is an OOD detector based on the Vendi Scores (VS), a family of similarity-based diversity metrics.<n>VNS is linear-time, non-parametric, and naturally combines class-conditional (local) and dataset-level (global) novelty signals.<n>VNS retains this performance when computed using only 1% of the training data, enabling deployment in memory- or access-constrained settings.
• Score: 5.450782029661113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out-of-distribution (OOD) detection is critical for the safe deployment of machine learning systems. 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 third paradigm and formulate OOD detection from a diversity perspective. We propose the Vendi Novelty Score (VNS), an OOD detector based on the Vendi Scores (VS), a family of similarity-based diversity metrics. VNS 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. VNS is linear-time, non-parametric, and naturally combines class-conditional (local) and dataset-level (global) novelty signals. Across multiple image classification benchmarks and network architectures, VNS achieves state-of-the-art OOD detection performance. Remarkably, VNS retains this performance when computed using only 1% of the training data, enabling deployment in memory- or access-constrained settings.

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