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I applied your NCE+ method to super_dimp.
As written in the paper, only the loss function was modified.
And both training and inference used super_dimp's method as it is.
However, it is performing worse than super_dimp.
(GOT-10K AUC - 0.801 vs 0.759)
Are there any other modifications not mentioned in the paper?
(Are the same values for the loss weight and learning rate used as the super_dimp method?)
The text was updated successfully, but these errors were encountered:
I applied your NCE+ method to super_dimp.
As written in the paper, only the loss function was modified.
And both training and inference used super_dimp's method as it is.
However, it is performing worse than super_dimp.
(GOT-10K AUC - 0.801 vs 0.759)
Are there any other modifications not mentioned in the paper?
(Are the same values for the loss weight and learning rate used as the super_dimp method?)
The text was updated successfully, but these errors were encountered: