ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables
In many anomaly detection tasks, where anomalous data rarely appear and are difficult to collect, training using only normal data is important. Although it is possible to manually create anomalous data using prior knowledge, they may be subject to user bias. In this paper, we propose an Anomalous La...
Main Authors: | Hironori Murase, Kenji Fukumizu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2022-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9761923/ |
Similar Items
-
IWGAN: Anomaly Detection in Airport Based on Improved Wasserstein Generative Adversarial Network
by: Ko-Wei Huang, et al.
Published: (2023-01-01) -
Unsupervised Anomaly Approach to Pedestrian Age Classification from Surveillance Cameras Using an Adversarial Model with Skip-Connections
by: Husnu Baris Baydargil, et al.
Published: (2021-10-01) -
Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets
by: Marzieh Esmaeili, et al.
Published: (2023-01-01) -
Efficient Anomaly Detection with Generative Adversarial Network for Breast Ultrasound Imaging
by: Tomoyuki Fujioka, et al.
Published: (2020-07-01) -
Semi-supervised generative adversarial networks for anomaly detection
by: Montenegro Juan, et al.
Published: (2022-01-01)