  Novelty detection aims to automatically identify out-of-distribution (OOD)
data, without any prior knowledge of them. It is a critical step in data
monitoring, behavior analysis and other applications, helping enable continual
learning in the field. Conventional methods of OOD detection perform
multi-variate analysis on an ensemble of data or features, and usually resort
to the supervision with OOD data to improve the accuracy. In reality, such
supervision is impractical as one cannot anticipate the anomalous data. In this
paper, we propose a novel, self-supervised approach that does not rely on any
pre-defined OOD data: (1) The new method evaluates the Mahalanobis distance of
the gradients between the in-distribution and OOD data. (2) It is assisted by a
self-supervised binary classifier to guide the label selection to generate the
gradients, and maximize the Mahalanobis distance. In the evaluation with
multiple datasets, such as CIFAR-10, CIFAR-100, SVHN and TinyImageNet, the
proposed approach consistently outperforms state-of-the-art supervised and
unsupervised methods in the area under the receiver operating characteristic
(AUROC) and area under the precision-recall curve (AUPR) metrics. We further
demonstrate that this detector is able to accurately learn one OOD class in
continual learning.
