Anomaly detection in multifactor data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00390571" target="_blank" >RIV/68407700:21230/24:00390571 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21340/24:00390571

Výsledek na webu

<a href="https://doi.org/10.1007/s00521-024-10291-2" target="_blank" >https://doi.org/10.1007/s00521-024-10291-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-024-10291-2" target="_blank" >10.1007/s00521-024-10291-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Anomaly detection in multifactor data

Popis výsledku v původním jazyce

In anomaly detection applications, anomalies might come from multiple sources and there might be many reasons why a sample is considered to be anomalous. However, most novel anomaly detection methods do not consider this. In our work, we describe a novel approach that is demonstrated on the problem of detection of anomalies in image data. We propose the SGVAEGAN model, which decomposes the image into three independent components—the shape of an object and its foreground and background textures—and provides anomaly scores for each of those factors separately. The overall anomaly score of an image is a weighted combination of the individual factor scores. The anomaly scores are learned in an unsupervised manner, and the weights are considered as hyperparameters that can be learned in the validation stage. The approach allows the identification of the source of the anomaly using factor scores, as well as the detection of semantic anomalies where the semantic meaning is encoded in the weights and learned from very few samples of validation anomalies. On classical anomaly detection benchmarks, the proposed model outperforms all baseline models. This is shown in a rigorous experimental study that covers the behavior of the model under a varying range of conditions.

Název v anglickém jazyce

Anomaly detection in multifactor data

Popis výsledku anglicky

In anomaly detection applications, anomalies might come from multiple sources and there might be many reasons why a sample is considered to be anomalous. However, most novel anomaly detection methods do not consider this. In our work, we describe a novel approach that is demonstrated on the problem of detection of anomalies in image data. We propose the SGVAEGAN model, which decomposes the image into three independent components—the shape of an object and its foreground and background textures—and provides anomaly scores for each of those factors separately. The overall anomaly score of an image is a weighted combination of the individual factor scores. The anomaly scores are learned in an unsupervised manner, and the weights are considered as hyperparameters that can be learned in the validation stage. The approach allows the identification of the source of the anomaly using factor scores, as well as the detection of semantic anomalies where the semantic meaning is encoded in the weights and learned from very few samples of validation anomalies. On classical anomaly detection benchmarks, the proposed model outperforms all baseline models. This is shown in a rigorous experimental study that covers the behavior of the model under a varying range of conditions.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neural Computing and Applications

ISSN

0941-0643

e-ISSN

1433-3058

Svazek periodika

36

Číslo periodika v rámci svazku

34

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

20

Strana od-do

21561-21580

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85203090669