Classification-Based Anomaly Detection for General Data

Liron Bergman, Yedid Hoshen

TL;DR: Anomaly detection method that uses: openset techniques for better generalization, random-transformation classification for non-image data.

Abstract: 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.

Keywords: anomaly detection

Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10), [CIFAR-100](https://paperswithcode.com/dataset/cifar-100), [UEA time-series datasets](https://paperswithcode.com/dataset/uea-time-series-datasets)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2005.02359/code)

Code: [ 2 community implementations](https://paperswithcode.com/paper/?openreview=H1lK_lBtvS)