Mahalanobis-Aware Training for Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2311.00808v1
• Date: Wed, 1 Nov 2023 19:46:40 GMT
• Title: Mahalanobis-Aware Training for Out-of-Distribution Detection
• Authors: Connor Mclaughlin, Jason Matterer, Michael Yee
• Abstract summary: We present a novel loss function and recipe for training networks with improved density-based out-of-distribution sensitivity. We demonstrate the effectiveness of our method on CIFAR-10, notably reducing the false-positive rate of the relative Mahalanobis distance method on far-OOD tasks by over 50%.
• Score: 0.11510009152620666
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: While deep learning models have seen widespread success in controlled environments, there are still barriers to their adoption in open-world settings. One critical task for safe deployment is the detection of anomalous or out-of-distribution samples that may require human intervention. In this work, we present a novel loss function and recipe for training networks with improved density-based out-of-distribution sensitivity. We demonstrate the effectiveness of our method on CIFAR-10, notably reducing the false-positive rate of the relative Mahalanobis distance method on far-OOD tasks by over 50%.

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