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Trying to learn Siamese networks for ranking tasks from here, I find it hard to understand why triplet loss was ever introduced at all, theoretically. I understand it works better in practice, but would like to understand.

The contrastive loss

$L(A, B) = y|f(A) - f(B)| + (1-y)max(0, m-|f(A) - F(B)|)$

would push similar samples together, and dissimilar samples apart.

The triplet loss

$L(A, P, N) = max(0, |f(A) - f(P)| - |f(A) - f(N)| + m) $

would push the positive close to the anchor, and the negative away from the anchor.


I fail to see the big difference, when to use one over the other, and why it is claimed in the video that triplet loss allows to learn a ranking, whereas contrastive loss only allows for similarity.

Would love a clarification on that

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