About ranking loss and EuclideanLoss

[irvingzhang0512]

Hi, thank you for your paper and code.

I read the paper and try to understand the model in train_val.prototxt .
As far as i know, final1 & final2 are both ranking functions, and labelr produces labels of {-1, 1}, then the ranking_loss is EuclideanLoss((final1 - final2) * labelr, 0)

my question is:

1. For me, EuclideanLoss((final1 - final2) * labelr, 0) is quite strange, because labelr seems to be useless in this loss function.
2. EuclideanLoss((final1 - final2) * labelr, 0) is quite different from the loss function in paper, which i think is like [ max(0, (final1 - final2) * labelr) ]^2.
3. For both of the above loss function, I think gradient vanishing will occur during training, because both final1 and final2 could be very small(say 1.1e-5, 1.2e-5), and the loss will be very small. Is that the case?

Did i miss something important?
Looking forward to your reply. Thanks again.