  We propose a one-class neural network (OC-NN) model to detect anomalies in
complex data sets. OC-NN combines the ability of deep networks to extract a
progressively rich representation of data with the one-class objective of
creating a tight envelope around normal data. The OC-NN approach breaks new
ground for the following crucial reason: data representation in the hidden
layer is driven by the OC-NN objective and is thus customized for anomaly
detection. This is a departure from other approaches which use a hybrid
approach of learning deep features using an autoencoder and then feeding the
features into a separate anomaly detection method like one-class SVM (OC-SVM).
The hybrid OC-SVM approach is sub-optimal because it is unable to influence
representational learning in the hidden layers. A comprehensive set of
experiments demonstrate that on complex data sets (like CIFAR and GTSRB), OC-NN
performs on par with state-of-the-art methods and outperformed conventional
shallow methods in some scenarios.
