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
