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项目作者： YeongHyeon

项目描述 ：
Implementation of Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection.
高级语言： Python
项目地址: git://github.com/YeongHyeon/MemAE.git
创建时间： 2020-02-11T08:34:40Z
项目社区：https://github.com/YeongHyeon/MemAE
开源协议：MIT License
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[TensorFlow] Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection

TensorFlow implementation of Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection. [PyTorch Version] [TensorFlow 2 Version]

Architecture



Architecture of MemAE.


Graph in TensorBoard



Graph of MemAE.


Problem Definition



‘Class-1’ is defined as normal and the others are defined as abnormal.


Results



Restoration result by MemAE.




Box plot and histogram of restoration loss in test procedure.


Environment

  • Python 3.7.4
  • Tensorflow 1.14.0
  • Numpy 1.17.1
  • Matplotlib 3.1.1
  • Scikit Learn (sklearn) 0.21.3

Reference

[1] Dong Gong et al. (2019). Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection. arXiv preprint arXiv:1904.02639.