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Table 7 Comparison experiment of weight selection based on deep-and-shallow loss and single loss

From: Spatial and temporal learning representation for end-to-end recording device identification

Loss function and parameters Network model End-to-end (ACC)
Categorical crossentropy loss DNN-LSTM 96.5%
  DNN + Bi-LSTM 96.6%
Deep-and-shallow loss (0.25:0.5:0.25) DNN-LSTM 97.3%
  DNN + Bi-LSTM 97.5%
Deep-and-shallow loss (0.4:0.2:0.4) DNN-LSTM 97.2%
  DNN + Bi-LSTM 97.2%
Deep-and-shallow loss (0.25:0.25:0.5) DNN-LSTM 97.1%
  DNN + Bi-LSTM 97.5%
Deep-and-shallow loss (0.2:0.6:0.2) DNN-LSTM 97.3%
  DNN + Bi-LSTM 97.3%