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Poster
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
Srishti Gautam · Ahcène Boubekki · Stine Hansen · Suaiba Salahuddin · Robert Jenssen · Marina Höhne · Michael Kampffmeyer

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #605
in Poster Session 6 »

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.

Keywords: interpretability · Deep Neural Networks · Explainable AI · Self-explaining Models
[ Paper]  [ Slides]  [ Poster]  [ OpenReview] 

Author Information

Srishti Gautam (UiT The Arctic University of Norway)
Ahcène Boubekki (UiT The Arctic University of Norway)

Explainable AI, Unsupervised Methods, Clustering, Representation Learning

Stine Hansen (UiT The Arctic University of Norway)
Suaiba Salahuddin (University of Tromsø)
Robert Jenssen (UiT The Arctic University of Norway)
Marina Höhne (TU Berlin)
Michael Kampffmeyer (UiT The Arctic University of Norway)

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