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  62  11.68% 
Person ReIdentification  39  7.34% 
Image Retrieval  27  5.08% 
General Classification  21  3.95% 
Face Recognition  20  3.77% 
Image Classification  15  2.82% 
FewShot Learning  12  2.26% 
Domain Adaptation  11  2.07% 
CrossModal Retrieval  11  2.07% 
Component  Type 


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