Classification-Based Anomaly Detection for General Data
This work addresses anomaly detection for general data types, offering a novel approach that improves applicability and performance, though it appears incremental in extending existing methods.
The paper tackled the problem of anomaly detection by proposing GOAD, a method that relaxes generalization assumptions and extends transformation-based techniques to non-image data, achieving state-of-the-art accuracy across multiple domains.
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.