Comments (3)
In cases like low-resource regime, freezing the encoder for n
steps sometimes achieve better performance than fine-tuning the whole model from the beginning. In steps after n
, the entire model is fine-tuned.
from av_hubert.
Hi,
Setting freeze-finetuning-updates
to n
will freeze the pre-trained encoder for n
steps during fine-tuning. See here.
from av_hubert.
Thank you for your answer!
I am not familiar with this type of freezing. What are the advantages of freezing the entire encoder for n steps?
Lastly, the entire pre-trained encoder is frozen for n steps, and then the rest of the steps are used to finetune the entire encoder, right?
from av_hubert.
Related Issues (20)
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