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 

Retrieval  76  9.57% 
Metric Learning  72  9.07% 
Person ReIdentification  43  5.42% 
Image Retrieval  31  3.90% 
Face Recognition  22  2.77% 
General Classification  21  2.64% 
Clustering  18  2.27% 
Image Classification  17  2.14% 
FewShot Learning  16  2.02% 
Component  Type 


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