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The need for interpretable models has fostered the development ofself-explainable classifiers. Prior approaches are either based on multi-stageoptimization schemes, impacting the predictive performance of the model, orproduce explanations that are not transparent, trustworthy or do not capturethe diversity of the data. To address these shortcomings, we propose ProtoVAE,a variational autoencoder-based framework that learns class-specific prototypesin an end-to-end manner and enforces trustworthiness and diversity byregularizing the representation space and introducing an orthonormalityconstraint. Finally, the model is designed to be transparent by directlyincorporating the prototypes into the decision process. Extensive comparisonswith previous self-explainable approaches demonstrate the superiority ofProtoVAE, highlighting its ability to generate trustworthy and diverseexplanations, while not degrading predictive performance.

ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model