Mahalanobis-Aware Training for Out-of-Distribution Detection
Connor Mclaughlin Jason Matterer Michael Yee
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%.