The goal of Triplet loss, in the context of Siamese Networks, is to maximize the joint probability among all scorepairs i.e. the product of all probabilities. By using its negative logarithm, we can get the loss formulation as follows:
$$ L_{t}\left(\mathcal{V}_{p}, \mathcal{V}_{n}\right)=\frac{1}{M N} \sum_{i}^{M} \sum_{j}^{N} \log \operatorname{prob}\left(v p_{i}, v n_{j}\right) $$
where the balance weight $1/MN$ is used to keep the loss with the same scale for different number of instance sets.
Source: Triplet Loss in Siamese Network for Object TrackingPaper  Code  Results  Date  Stars 

Task  Papers  Share 

Metric Learning  54  12.80% 
Person ReIdentification  37  8.77% 
Image Retrieval  23  5.45% 
General Classification  22  5.21% 
Face Recognition  17  4.03% 
FewShot Learning  12  2.84% 
Image Classification  11  2.61% 
CrossModal Retrieval  8  1.90% 
Semantic Segmentation  7  1.66% 
Component  Type 


🤖 No Components Found  You can add them if they exist; e.g. Mask RCNN uses RoIAlign 