Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method to relax current generalization assumptions. Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations. Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types. The strong performance of our method is extensively validated on multiple datasets from different domains.
An anomaly detection that:  uses random-transformation classification for generalizing to non-image data. This paper proposes a deep method for anomaly detection that unifies recent deep one-class classification and transformation-based classification approaches. This paper proposes an approach to classification-based anomaly detection for general data by using the affine transformation y = Wx+b.