A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection Using Compounded Corruptions

Abstract

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 test time. In most practical settings OOD examples are not known at 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 C ompou n ded C orruption (CnC) technique for the OOD data augmentation. One of the major advantages of CnC is that it does not require any hold-out data apart from 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. 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.