  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, GOAD, 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.
