ConNet: Deep Semi-Supervised Anomaly Detection Based on Sparse Positive Samples

ConNet: Deep Semi-Supervised Anomaly Detection Based on Sparse Positive Samples

Existing semi-supervised anomaly detection methods usually use a large amount of labeled normal data for training, which have the problem of high labeling costs. Only a few semi-supervised methods utilize unlabeled data and a few labeled anomalies to train models. However, these kinds of methods usu...

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Bibliographic Details
Main Authors: Feng Gao, Jing Li, Ruiying Cheng, Yi Zhou, Ying Ye
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Data mining
anomaly detection
deep learning
outlier detection
Online Access:https://ieeexplore.ieee.org/document/9420659/

Internet

https://ieeexplore.ieee.org/document/9420659/

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