Unsupervised Anomaly Detection with Distillated Teacher-Student Network Ensemble

We address the problem of unsupervised anomaly detection for multivariate data. Traditional machine learning based anomaly detection algorithms rely on specific assumptions of normal patterns and fail to model complex feature interactions and relations. Recently, existing deep learning based methods...

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Detalles Bibliográficos
Autores principales: Xiao, Qinfeng, Wang, Jing, Lin, Youfang, Gongsa, Wenbo, Hu, Ganghui, Li, Menggang, Wang, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7915583/
https://www.ncbi.nlm.nih.gov/pubmed/33561954
http://dx.doi.org/10.3390/e23020201

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7915583/
https://www.ncbi.nlm.nih.gov/pubmed/33561954
http://dx.doi.org/10.3390/e23020201

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