Counterfeit Anomaly Using Generative Adversarial Network for Anomaly Detection

Counterfeit Anomaly Using Generative Adversarial Network for Anomaly Detection

Anomaly detection aims to detect anomaly with only normal data available for training. It attracts considerable attentions in the medical domain, as normal data is relatively easy to obtain but it is rather difficult to have abnormal data especially for some rare diseases, making training a standard...

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Bibliographic Details
Main Authors: Haocheng Shen, Jingkun Chen, Ruixuan Wang, Jianguo Zhang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Anomaly detection
concentration loss
fake pool
GAN
Online Access:https://ieeexplore.ieee.org/document/9144563/

Internet

https://ieeexplore.ieee.org/document/9144563/

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