  Prototypical Learning is based on the idea that there is a point (which we
call prototype) around which the embeddings of a class are clustered. It has
shown promising results in scenarios with little labeled data or to design
explainable models. Typically, prototypes are either defined as the average of
the embeddings of a class or are designed to be trainable. In this work, we
propose to predefine prototypes following human-specified criteria, which
simplify the training pipeline and brings different advantages. Specifically,
in this work we explore two of these advantages: increasing the inter-class
separability of embeddings and disentangling embeddings with respect to
different variance factors, which can translate into the possibility of having
explainable predictions. Finally, we propose different experiments that help to
understand our proposal and demonstrate empirically the mentioned advantages.
