Comparison of Anomaly Detectors: Context Matters
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00352126" target="_blank" >RIV/68407700:21230/22:00352126 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/22:00352126
Result on the web
<a href="https://doi.org/10.1109/TNNLS.2021.3116269" target="_blank" >https://doi.org/10.1109/TNNLS.2021.3116269</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TNNLS.2021.3116269" target="_blank" >10.1109/TNNLS.2021.3116269</a>
Alternative languages
Result language
angličtina
Original language name
Comparison of Anomaly Detectors: Context Matters
Original language description
Deep generative models are challenging the classical methods in the field of anomaly detection nowadays. Every newly published method provides evidence of outperforming its predecessors, sometimes with contradictory results. The objective of this article is twofold: to compare anomaly detection methods of various paradigms with a focus on deep generative models and identification of sources of variability that can yield different results. The methods were compared on popular tabular and image datasets. We identified that the main sources of variability are the experimental conditions: 1) the type of dataset (tabular or image) and the nature of anomalies (statistical or semantic) and 2) strategy of selection of hyperparameters, especially the number of available anomalies in the validation set. Methods perform differently in different contexts, i.e., under a different combination of experimental conditions together with computational time. This explains the variability of the previous results and highlights the importance of careful specification of the context in the publication of a new method. All our code and results are available for download.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science 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)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Neural Networks and Learning Systems
ISSN
2162-237X
e-ISSN
2162-2388
Volume of the periodical
33
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
2494-2507
UT code for WoS article
000732313900001
EID of the result in the Scopus database
2-s2.0-85117345443