  Modern deep neural network models are known to erroneously classify
out-of-distribution (OOD) test data into one of the in-distribution (ID)
training classes with high confidence. This can have disastrous consequences
for safety-critical applications. A popular mitigation strategy is to train a
separate classifier that can detect such OOD samples at the test time. In most
practical settings OOD examples are not known at the train time, and hence a
key question is: how to augment the ID data with synthetic OOD samples for
training such an OOD detector? In this paper, we propose a novel Compounded
Corruption technique for the OOD data augmentation termed CnC. One of the major
advantages of CnC is that it does not require any hold-out data apart from the
training set. Further, unlike current state-of-the-art (SOTA) techniques, CnC
does not require backpropagation or ensembling at the test time, making our
method much faster at inference. Our extensive comparison with 20 methods from
the major conferences in last 4 years show that a model trained using CnC based
data augmentation, significantly outperforms SOTA, both in terms of OOD
detection accuracy as well as inference time. We include a detailed post-hoc
analysis to investigate the reasons for the success of our method and identify
higher relative entropy and diversity of CnC samples as probable causes. We
also provide theoretical insights via a piece-wise decomposition analysis on a
two-dimensional dataset to reveal (visually and quantitatively) that our
approach leads to a tighter boundary around ID classes, leading to better
detection of OOD samples. Source code link: https://github.com/cnc-ood
