  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.
