Predefined Prototypes for Intra-Class Separation and Disentanglement

It is possible to associate some concrete dimensions of these representations with concrete human-understandable features Prototypical Learning is based on the idea that there is a point so that a change of a feature produces changes in only a few (which we call prototype) around which the embeddings of a dimensions of the space. This is has some advantages such as class are clustered. It has shown promising results in scenarios (i) having more control over data creation in generative models with little labeled data or to design explainable models. Typically, [8], or (ii) providing the ability to explain and interpret prototypes are either defined as the average of the embeddings model predictions [9]. of a class or are designed to be trainable. In this work, In this paper we propose a modification on the prototypical we propose to predefine prototypes following human-specified systems that preserves their default advantages and, in addition, criteria, which simplify the training pipeline and brings different allows solving the two problems presented.