Iterative Deployment Exposure for Unsupervised Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2406.02327v2
• Date: Mon, 19 May 2025 14:30:00 GMT
• Title: Iterative Deployment Exposure for Unsupervised Out-of-Distribution Detection
• Authors: Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila,
• Abstract summary: Iterative Deployment Exposure (IDE) is a novel and more realistic setting for out-of-distribution (OOD) detection.<n> CSO uses a new U-OOD scoring function that combines the Mahalanobis distance with a nearest-neighbor approach.<n>We validate our approach on a dedicated benchmark, showing that our method greatly improves upon strong baselines on three medical imaging modalities.
• Score: 5.019613806273252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models are vulnerable to performance degradation when encountering out-of-distribution (OOD) images, potentially leading to misdiagnoses and compromised patient care. These shortcomings have led to great interest in the field of OOD detection. Existing unsupervised OOD (U-OOD) detection methods typically assume that OOD samples originate from an unconcentrated distribution complementary to the training distribution, neglecting the reality that deployed models passively accumulate task-specific OOD samples over time. To better reflect this real-world scenario, we introduce Iterative Deployment Exposure (IDE), a novel and more realistic setting for U-OOD detection. We propose CSO, a method for IDE that starts from a U-OOD detector that is agnostic to the OOD distribution and slowly refines it during deployment using observed unlabeled data. CSO uses a new U-OOD scoring function that combines the Mahalanobis distance with a nearest-neighbor approach, along with a novel confidence-scaled few-shot OOD detector to effectively learn from limited OOD examples. We validate our approach on a dedicated benchmark, showing that our method greatly improves upon strong baselines on three medical imaging modalities.

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