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异常检测-1-综述-Deep Learning for Anomaly Detection: A Survey

发布时间 2023-04-04 10:01:55作者: 宋岳庭

https://www.researchgate.net/publication/330357393_Deep_Learning_for_Anomaly_Detection_A_Survey?enrichId=rgreq-40000b66a80039399492f90066ec07a0-XXX&enrichSource=Y292ZXJQYWdlOzMzMDM1NzM5MztBUzo3MTU2NTQ5MTA5Njc4MDhAMTU0NzYzNjgzNTAyMw%3D%3D&el=1_x_3&_esc=publicationCoverPdf

时序数据的深度异常检测模型，主要有CNN, RNN, LSTM

时序数据的异常检测：
Table 15: Examples of DAD techniques used in industrial operations.

  • CNN: Convolution Neural Networks
  • LSTM : Long Short Term Memory Networks
  • GRU: Gated Recurrent Unit
  • DNN : Deep Neural Networks
  • AE: Autoencoders
  • DAE: Denoising Autoencoders
  • SVM: Support Vector Machines
  • SDAE: Stacked Denoising Autoencoders
  • RNN : Recurrent Neural Networks.
  • DNN-SVM：Hybrid Models

Table 16: Examples of DAD techniques used in time series data.

  • CNN: Convolution Neural Networks,
  • GAN: Generative Adversarial networks,
  • LSTM : Long Short Term Memory Networks
  • GRU: Gated Recurrent Unit,
  • DNN : Deep Neural Networks,
  • AE: Autoencoders,
  • DAE: Denoising Autoencoders,
  • VAE: Variational Autoencoder
  • SDAE: Stacked Denoising Autoencoders

https://blog.csdn.net/aiyouyou_/article/details/120177814