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Thanks for the great work. I think gradient normalization is a reasonable idea to extend GC. But I notice 2 points which is confused to me:
The gradient which size is greater than 1 is centralized by the mean and all the gradient (which is not filtered by the size ) are normalized by the std, is this an empirically better implementation or it is just a bug.
Also I notice the calculation dimension of mean and std in gradient normalization is different, which is not very intuitive to me.
Thanks for the reply.
The text was updated successfully, but these errors were encountered:
Hi,
Thanks for the great work. I think gradient normalization is a reasonable idea to extend GC. But I notice 2 points which is confused to me:
The gradient which size is greater than 1 is centralized by the mean and all the gradient (which is not filtered by the size ) are normalized by the std, is this an empirically better implementation or it is just a bug.
Also I notice the calculation dimension of mean and std in gradient normalization is different, which is not very intuitive to me.
Thanks for the reply.
The text was updated successfully, but these errors were encountered: