Anomaly detection in multifactor data

Result code in 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>

Alternative codes found

RIV/68407700:21340/24:00390571

Result on the web

<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>

Result language

angličtina

Original language name

Anomaly detection in multifactor data

Original language description

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.

Czech name

—

Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

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

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

Neural Computing and Applications

ISSN

0941-0643

e-ISSN

1433-3058

Volume of the periodical

36

Issue of the periodical within the volume

34

Country of publishing house

GB - UNITED KINGDOM

Number of pages

20

Pages from-to

21561-21580

UT code for WoS article

—

EID of the result in the Scopus database

2-s2.0-85203090669