Out-of-Distribution Detection Using an Ensemble
of Self Supervised Leave-out Classifiers
Abstract. As deep learning methods form a critical part in commercially
important applications such as autonomous driving and medical diagnostics, it is important to reliably detect out-of-distribution (OOD) inputs
while employing these algorithms. In this work, we propose an OOD
detection algorithm which comprises of an ensemble of classifiers. We
train each classifier in a self-supervised manner by leaving out a random
subset of training data as OOD data and the rest as in-distribution (ID)
data. We propose a novel margin-based loss over the softmax output
which seeks to maintain at least a margin m between the average entropy
of the OOD and in-distribution samples. In conjunction with the standard
cross-entropy loss, we minimize the novel loss to train an ensemble of
classifiers. We also propose a novel method to combine the outputs of the
ensemble of classifiers to obtain OOD detection score and class prediction.
Overall, our method convincingly outperforms Hendrycks et al. [7] and the
current state-of-the-art ODIN [13] on several OOD detection benchmarks.