Comparison of Anomaly Detectors: Context Matters

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21340/22:00352126

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Anomaly Detectors: Context Matters

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Comparison of Anomaly Detectors: Context Matters

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Neural Networks and Learning Systems

ISSN

2162-237X

e-ISSN

2162-2388

Svazek periodika

33

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

2494-2507

Kód UT WoS článku

000732313900001

EID výsledku v databázi Scopus

2-s2.0-85117345443