Tag: ood
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Perturbations in the Orthogonal Complement Subspace for Efficient Out-of-Distribution Detection
Perturbations in the Orthogonal Complement Subspace for Efficient Out-of-Distribution Detection arXiv:2511.00849v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for deploying deep learning models in open-world environments. Existing approaches, such as energy-based scoring and gradient-projection methods, typically rely on high-dimensional representations to separate in-distribution (ID) and OOD samples. We introduce P-OCS (Perturbations in the…
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A Multi-dimensional Semantic Surprise Framework Based on Low-Entropy Semantic Manifolds for Fine-Grained Out-of-Distribution Detection
A Multi-dimensional Semantic Surprise Framework Based on Low-Entropy Semantic Manifolds for Fine-Grained Out-of-Distribution Detection arXiv:2510.13093v1 Announce Type: new Abstract: Out-of-Distribution (OOD) detection is a cornerstone for the safe deployment of AI systems in the open world. However, existing methods treat OOD detection as a binary classification problem, a cognitive flattening that fails to distinguish between…