I was going through your code to understand how you have used each of the 4 datasets for cross dataset training and testing.
Observations:
As per the code in the data_merge file, you are loading the target dataset as your test set (which seems fine).
However, you seem to be using the target dataset as your validation set to compute the HTER and AUC scores after every epoch, and choose the epoch with the best score on the target dataset. training file for reference. Example: Lets choose OCIM protocol. For every epoch, you seem to be training on OCI and testing on M and choosing the epoch with the best score on M.
Questions:
Is this approach valid ? Shouldn't there be a validation set combining O-C-I, that is used to evaluate during training and finally the chosen model is tested on M to compute the HTER and AUC scores ?
Dear Authors Thanks for the great work.
I was going through your code to understand how you have used each of the 4 datasets for cross dataset training and testing.
Observations:
As per the code in the data_merge file, you are loading the target dataset as your test set (which seems fine).
However, you seem to be using the target dataset as your validation set to compute the HTER and AUC scores after every epoch, and choose the epoch with the best score on the target dataset. training file for reference. Example: Lets choose OCIM protocol. For every epoch, you seem to be training on OCI and testing on M and choosing the epoch with the best score on M.
Questions:
Kindly request you to clarify the same.
Thanks