Performance of Triplet loss network vs multiclass classification
For me triplet loss function (as mentioned by Neil Slater as well), is used for object recognition i.e. identify the similar object. Face-recognition is one of the use case.


  This function is based upon Siamese Network, which will provide
  us the feature vector as an output. During recognition, we compare the
  feature-vector of the new image with the feature-vector of the
  training data. Select the one which has closer matching.


In order to improve Triplet-loss function, you can try different similarity approaches or different threshold value's. Please do share the output.

But again for me it's not comparable with object-classification use case.