  Deep metric learning is a technique used in a variety of discriminative tasks
to achieve zero-shot, one-shot or few-shot learning. When applied, the system
learns an embedding space where a non-parametric approach, such as \gls{knn},
can be used to discriminate features during test time. This work focuses on
investigating to what extent feature information contained within this
embedding space can be used to carry out sub-discrimination in the feature
space. The study shows that within a discrimination embedding, the information
on the salient attributes needed to solve the problem of sub-discrimination is
saved within the embedding and that this inherent information can be used to
carry out sub-discriminative tasks. To demonstrate this, an embedding designed
initially to discriminate faces is used to differentiate several attributes
such as gender, age and skin tone, without any additional training. The study
is split into two study cases: intra class discrimination where all the
embeddings took into consideration are from the same identity; and extra class
discrimination where the embeddings represent different identities. After the
study, it is shown that it is possible to infer common attributes to different
identities. The system can also perform extra class sub-discrimination with a
high accuracy rate, notably 99.3\%, 99.3\% and 94.1\% for gender, skin tone,
and age, respectively. Intra class tests show more mixed results with more
nuanced attributes like emotions not being reliably classified, while more
distinct attributes such as thick-framed glasses and beards, achieving 97.2\%
and 95.8\% accuracy, respectively.
