ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model

• URL: http://arxiv.org/abs/2210.08151v1
• Date: Sat, 15 Oct 2022 00:42:13 GMT
• Title: ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
• Authors: Srishti Gautam, Ahcene Boubekki, Stine Hansen, Suaiba Amina Salahuddin, Robert Jenssen, Marina MC H\"ohne, Michael Kampffmeyer
• Abstract summary: ProtoVAE is a variational autoencoder-based framework that learns class-specific prototypes in an end-to-end manner. It enforces trustworthiness and diversity by regularizing the representation space and introducing an orthonormality constraint.
• Score: 18.537838366377915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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