  The need for interpretable models has fostered the development of
self-explainable classifiers. Prior approaches are either based on multi-stage
optimization schemes, impacting the predictive performance of the model, or
produce explanations that are not transparent, trustworthy or do not capture
the diversity of the data. To address these shortcomings, we propose ProtoVAE,
a variational autoencoder-based framework that learns class-specific prototypes
in an end-to-end manner and enforces trustworthiness and diversity by
regularizing the representation space and introducing an orthonormality
constraint. Finally, the model is designed to be transparent by directly
incorporating the prototypes into the decision process. Extensive comparisons
with previous self-explainable approaches demonstrate the superiority of
ProtoVAE, highlighting its ability to generate trustworthy and diverse
explanations, while not degrading predictive performance.
