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- W3171163219 abstract "Mahalanobis distance (MD) is a simple and popular post-processing method for detecting out-of-distribution (OOD) inputs in neural networks. We analyze its failure modes for near-OOD detection and propose a simple fix called relative Mahalanobis distance (RMD) which improves performance and is more robust to hyperparameter choice. On a wide selection of challenging vision, language, and biology OOD benchmarks (CIFAR-100 vs CIFAR-10, CLINC OOD intent detection, Genomics OOD), we show that RMD meaningfully improves upon MD performance (by up to 15% AUROC on genomics OOD)." @default.
- W3171163219 created "2021-06-22" @default.
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- W3171163219 date "2021-06-16" @default.
- W3171163219 modified "2023-10-03" @default.
- W3171163219 title "A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection" @default.
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- W3171163219 doi "https://doi.org/10.48550/arxiv.2106.09022" @default.
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