Training Autoencoder using Three Different Reversed Color Models for Anomaly Detection
書誌事項
- タイトル別名
-
- Training autoencoder using three different reversed color models for anomaly detection
抄録
Autoencoders (AEs) have been applied in several applications such as anomaly detectors and object recognition systems. However, although the recent neural networks have relatively high accuracy but sometimes false detection may occur. This paper introduces AE as an anomaly detector. The proposed AE is trained using both normal and anomalous data based on convolutional neural network with three different color models Hue Saturation Value (HSV), Red Green Blue (RGB), and our own model (TUV). As a result, the trained AE reconstruct the normal images without change, whereas the anomalous image would be reconstructed reversely. The training and testing of the AE in case of RGB, HSV, and TUV color models were demonstrated and Cifar-10 dataset had been used for the evaluation process. It can be noticed that HSV color model has been more effective and achievable as an anomaly detector rather than other color models based on Z- and F-test analyses.
収録刊行物
-
- Journal of Robotics, Networking and Artificial Life
-
Journal of Robotics, Networking and Artificial Life 7 (1), 35-40, 2020-05-20
Atlantis Press
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1050006662390617216
-
- NII論文ID
- 120007035847
-
- ISSN
- 24059021
- 23526386
-
- HANDLE
- 10228/00008261
-
- 本文言語コード
- en
-
- 資料種別
- journal article
-
- データソース種別
-
- IRDB
- Crossref
- CiNii Articles
- KAKEN