NFAD: fixing anomaly detection using normalizing flows

Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics. Most of the conventional approaches to anomaly detection, such as one-cla...

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Autores principales:	Artem Ryzhikov, Maxim Borisyak, Andrey Ustyuzhanin, Denis Derkach
Formato:	article
Lenguaje:	EN
Publicado:	PeerJ Inc. 2021
Materias:	Anomaly detection Deep learning Semi-supervised learning Normalizing flows Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/3a3dd334749642418df3f04c98b798a8
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id	oai:doaj.org-article:3a3dd334749642418df3f04c98b798a8
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spelling	oai:doaj.org-article:3a3dd334749642418df3f04c98b798a82021-11-20T15:05:09ZNFAD: fixing anomaly detection using normalizing flows10.7717/peerj-cs.7572376-5992https://doaj.org/article/3a3dd334749642418df3f04c98b798a82021-11-01T00:00:00Zhttps://peerj.com/articles/cs-757.pdfhttps://peerj.com/articles/cs-757/https://doaj.org/toc/2376-5992Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics. Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Auto-Encoder, are one-class classification methods, i.e., focus on separating normal data from the rest of the space. Such methods are based on the assumption of separability of normal and anomalous classes, and subsequently do not take into account any available samples of anomalies. Nonetheless, in practical settings, some anomalous samples are often available; however, usually in amounts far lower than required for a balanced classification task, and the separability assumption might not always hold. This leads to an important task—incorporating known anomalous samples into training procedures of anomaly detection models. In this work, we propose a novel model-agnostic training procedure to address this task. We reformulate one-class classification as a binary classification problem with normal data being distinguished from pseudo-anomalous samples. The pseudo-anomalous samples are drawn from low-density regions of a normalizing flow model by feeding tails of the latent distribution into the model. Such an approach allows to easily include known anomalies into the training process of an arbitrary classifier. We demonstrate that our approach shows comparable performance on one-class problems, and, most importantly, achieves comparable or superior results on tasks with variable amounts of known anomalies.Artem RyzhikovMaxim BorisyakAndrey UstyuzhaninDenis DerkachPeerJ Inc.articleAnomaly detectionDeep learningSemi-supervised learningNormalizing flowsElectronic computers. Computer scienceQA75.5-76.95ENPeerJ Computer Science, Vol 7, p e757 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Anomaly detection Deep learning Semi-supervised learning Normalizing flows Electronic computers. Computer science QA75.5-76.95
spellingShingle	Anomaly detection Deep learning Semi-supervised learning Normalizing flows Electronic computers. Computer science QA75.5-76.95 Artem Ryzhikov Maxim Borisyak Andrey Ustyuzhanin Denis Derkach NFAD: fixing anomaly detection using normalizing flows
description	Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics. Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Auto-Encoder, are one-class classification methods, i.e., focus on separating normal data from the rest of the space. Such methods are based on the assumption of separability of normal and anomalous classes, and subsequently do not take into account any available samples of anomalies. Nonetheless, in practical settings, some anomalous samples are often available; however, usually in amounts far lower than required for a balanced classification task, and the separability assumption might not always hold. This leads to an important task—incorporating known anomalous samples into training procedures of anomaly detection models. In this work, we propose a novel model-agnostic training procedure to address this task. We reformulate one-class classification as a binary classification problem with normal data being distinguished from pseudo-anomalous samples. The pseudo-anomalous samples are drawn from low-density regions of a normalizing flow model by feeding tails of the latent distribution into the model. Such an approach allows to easily include known anomalies into the training process of an arbitrary classifier. We demonstrate that our approach shows comparable performance on one-class problems, and, most importantly, achieves comparable or superior results on tasks with variable amounts of known anomalies.
format	article
author	Artem Ryzhikov Maxim Borisyak Andrey Ustyuzhanin Denis Derkach
author_facet	Artem Ryzhikov Maxim Borisyak Andrey Ustyuzhanin Denis Derkach
author_sort	Artem Ryzhikov
title	NFAD: fixing anomaly detection using normalizing flows
title_short	NFAD: fixing anomaly detection using normalizing flows
title_full	NFAD: fixing anomaly detection using normalizing flows
title_fullStr	NFAD: fixing anomaly detection using normalizing flows
title_full_unstemmed	NFAD: fixing anomaly detection using normalizing flows
title_sort	nfad: fixing anomaly detection using normalizing flows
publisher	PeerJ Inc.
publishDate	2021
url	https://doaj.org/article/3a3dd334749642418df3f04c98b798a8
work_keys_str_mv	AT artemryzhikov nfadfixinganomalydetectionusingnormalizingflows AT maximborisyak nfadfixinganomalydetectionusingnormalizingflows AT andreyustyuzhanin nfadfixinganomalydetectionusingnormalizingflows AT denisderkach nfadfixinganomalydetectionusingnormalizingflows
_version_	1718419417745850368

NFAD: fixing anomaly detection using normalizing flows

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