  Anomaly detection is a challenging problem in machine learning, and is even
more so when dealing with instances that are captured in low-level, raw data
representations without a well-behaved set of engineered features. The Radial
Basis Function Data Descriptor (RBFDD) network is an effective solution for
anomaly detection, however, it is a shallow model that does not deal
effectively with raw data representations. This paper investigates approaches
to modifying the RBFDD network to transform it into a deep one-class classifier
suitable for anomaly detection problems with low-level raw data
representations. We show that approaches based on transfer learning are not
effective and our results suggest that this is because the latent
representations learned by generic classification models are not suitable for
anomaly detection. Instead we show that an approach that adds multiple
convolutional layers before the RBF layer, to form a Deep Radial Basis Function
Data Descriptor (D-RBFDD) network, is very effective. This is shown in a set of
evaluation experiments using multiple anomaly detection scenarios created from
publicly available image classification datasets, and a real-world anomaly
detection dataset in which different types of arrhythmia are detected in
electrocardiogram (ECG) data. Our experiments show that the D-RBFDD network
out-performs state-of-the-art anomaly detection methods including the Deep
Support Vector Data Descriptor (Deep SVDD), One-Class SVM, and Isolation Forest
on the image datasets, and produces competitive results for the ECG dataset.
