NFAD: fixing anomaly detection using normalizing flows
Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics. Most of the conventional approaches to anomaly detection, such as one-cla...
Guardado en:
| Autores principales: | Artem Ryzhikov, Maxim Borisyak, Andrey Ustyuzhanin, Denis Derkach |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
PeerJ Inc.
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/3a3dd334749642418df3f04c98b798a8 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Self-Supervised Learning for Anomaly Detection With Dynamic Local Augmentation
por: Seungdong Yoa, et al.
Publicado: (2021) -
Deep learning-based anomaly-onset aware remaining useful life estimation of bearings
por: Pooja Vinayak Kamat, et al.
Publicado: (2021) -
Evaluation of semi-supervised learning using sparse labeling to segment cell nuclei
por: Bruch Roman, et al.
Publicado: (2020) -
Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis
por: Mingzhu Tang, et al.
Publicado: (2021) -
Anomaly sign detection by monitoring thousands of process values using a two-stage autoencoder
por: Susumu NAITO, et al.
Publicado: (2021)