Comments (2)
zhangrong1722
commented on June 6, 2024
After checking the definition of semihard triplets.I have the same suggestion as you.Have you confirmed this by experiments?
I found the original version may result in mode collapse,i.e.,the distance are nan after a few epochs training.
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adambielski
commented on June 6, 2024
@aikudexiaohai We need to use the same definition of loss as we use when computing gradients, not sure why you would lose the margin there.
In semihard example, by the definition of the loss it can't be negative. So I don't know what you're suggesting.
There may be many variations of semi-hard negatives, the definition I used here was from the FaceNet paper.
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Related Issues (20)
- Citing Work
- how to classify
- Tripletloss finally returns to zero HOT 1
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- utils.py:FunctionNegativeTripletSelector - 'anchor_positive' referenced before assignment when len(label_indices) < 2 HOT 6
- Implementation of Triplet loss on CASIA Web Face Dataset
- training
- References HOT 1
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