CLASSIFICATION-BASED ANOMALY DETECTION FOR GENERAL DATA

Liron Bergman, Yedid Hoshen

Research output: Contribution to conferencePaperpeer-review

168 Scopus citations

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.

Original languageAmerican English
StatePublished - 2020
Event8th International Conference on Learning Representations, ICLR 2020 - Addis Ababa, Ethiopia
Duration: 30 Apr 2020 → …

Conference

Conference8th International Conference on Learning Representations, ICLR 2020
Country/TerritoryEthiopia
CityAddis Ababa
Period30/04/20 → …

Bibliographical note

Publisher Copyright:
© 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

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