arXiv:2106.09022
공식 코드 · Papers with Code
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
원문 초록
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).
함께 살펴볼 논문
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FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection
Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentation
Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selections and Approximations
Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature Masking
Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations
Leveraging Perturbation Robustness to Enhance Out-of-Distribution Detection
토론
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