A Fair Classifier Embracing Triplet Collapse
A. Martzloff N. Posocco Q. Ferré
In this paper, we study the behaviour of the triplet loss and show that it
can be exploited to limit the biases created and perpetuated by machine
learning models. Our fair classifier uses the collapse of the triplet loss when
its margin is greater than the maximum distance between two points in the
latent space, in the case of stochastic triplet selection.