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Identifies inputs that differ from the training data distribution. Enhances model safety and reliability in real-world applications.
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 General OOD Detection via Model-aware and Subspace-aware Variable Priority Min Lu Hemant Ishwaran | |||
 Predictive Sample Assignment for Semantically Coherent Out-of-Distribution Detection Zhimao Peng Enguang Wang Xialei Liu Ming-Ming Cheng | |||
 Disentangled and Distilled Encoder for Out-of-Distribution Reasoning with Rademacher Guarantees Zahra Rahiminasab Michael Yuhas Arvind Easwaran | |||
 Neural Coherence : Find higher performance to out-of-distribution tasks from few samples Simon Guiroy Mats Richter Sarath Chandar Christopher Pal | |||
 TIE: A Training-Inversion-Exclusion Framework for Visually Interpretable and Uncertainty-Guided Out-of-Distribution Detection Pirzada Suhail Rehna Afroz Amit Sethi | |||
 SupLID: Geometrical Guidance for Out-of-Distribution Detection in Semantic SegmentationInternational Conference on Information and Knowledge Management (CIKM), 2025 | |||
Exploiting Inter-Sample Information for Long-tailed Out-of-Distribution DetectionIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2025 | |||
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