  In this contribution, we augment the metric learning setting by introducing a
parametric pseudo-distance, trained jointly with the encoder. Several
interpretations are thus drawn for the learned distance-like model's output. We
first show it approximates a likelihood ratio which can be used for hypothesis
tests, and that it further induces a large divergence across the joint
distributions of pairs of examples from the same and from different classes.
Evaluation is performed under the verification setting consisting of
determining whether sets of examples belong to the same class, even if such
classes are novel and were never presented to the model during training.
Empirical evaluation shows such method defines an end-to-end approach for the
verification problem, able to attain better performance than simple scorers
such as those based on cosine similarity and further outperforming widely used
downstream classifiers. We further observe training is much simplified under
the proposed approach compared to metric learning with actual distances,
requiring no complex scheme to harvest pairs of examples.
