Classification-Based Anomaly Detection for General Data
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.