Mahalanobis-Aware Training for Out-of-Distribution Detection
This addresses the critical need for reliable anomaly detection in open-world settings, though it appears incremental as it builds upon existing Mahalanobis distance methods.
The paper tackled the problem of detecting out-of-distribution samples for safe deep learning deployment by introducing a novel loss function and training recipe, resulting in a reduction of the false-positive rate by over 50% on CIFAR-10 far-OOD tasks.
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%.