ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
This addresses the need for transparent and trustworthy interpretable models in machine learning, representing an incremental improvement over prior methods.
The paper tackles the problem of creating interpretable self-explainable classifiers by proposing ProtoVAE, a variational autoencoder-based framework that learns class-specific prototypes end-to-end, resulting in trustworthy and diverse explanations without degrading predictive performance.
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.