OK, yes I am familiar with that paper, but that model has a catch: the triplets are not just randomly sampled from classes, but instead there are humans raters involved making sure the anchor and positive are truly similar. So that is a lot of extra work compared to simply training a softmax + cross entropy model + KNN.
For example, in their Figure 1 there is a triplet of 3 lamps, let’s say all three are from the same “lamp” class. Human raters say the positive and anchor are more similar than the negative. This is not what you get if you simply construct triplets at random, taking the anchor and positive from the same class and the negative from another class entirely.
With human-raters forming triplets, I suspect the triplet ranking would be better, but for simple random triplets, the ranking might perform similarly to what you get from the before-last layer of a softmax+cross entropy.
I guess I just have to try both and see, thanks for helping out!