云鉴权>> MemAE-TF2>> 返回
项目作者： YeongHyeon

项目描述 ：
TensorFlow 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-TF2.git
创建时间： 2020-02-20T14:01:59Z
项目社区：https://github.com/YeongHyeon/MemAE-TF2
开源协议：MIT License
下载


[TensorFlow 2] 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 1 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 2.1.0
  • Numpy 1.18.1
  • Matplotlib 3.1.3
  • Scikit Learn (sklearn) 0.22.1

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.