HyperPg - Prototypical Gaussians on the Hypersphere for Interpretable Deep Learning

Maximilian Xiling Li, Korbinian Franz Rudolf, Nils Blank, Rudolf Lioutikov

Keywords: XAI, Inherently Interpretable Model, Prototype Learning, This looks like That Framework, Image Classification, Deep Learning

TL;DR: HyperPg aligns Gaussian Prototypes with Human-Labeled Concepts for interpretable image classification.

Abstract:

Prototype Learning methods provide an interpretable alternative to black-box deep learning models. Approaches such as ProtoPNet learn, which part of a test image "look like"' known prototypical parts from training images, combining predictive power with the inherent interpretability of case-based reasoning. However, existing approaches have two main drawbacks: A) They rely solely on deterministic similarity scores without statistical confidence. B) The prototypes are learned in a black-box manner without human input.

This work introduces HyperPg, a new prototype representation leveraging Gaussian distributions on a hypersphere in latent space, with learnable mean and variance. HyperPg prototypes adapt to the spread of clusters in the latent space and output likelihood scores.The new architecture, HyperPgNet, leverages HyperPg to learn prototypes aligned with human concepts from pixel-level annotations. Consequently, each prototype represents a specific concept such as color, image texture, or part of the image subject. A concept extraction pipeline built on foundation models provides pixel-level annotations, significantly reducing human labeling effort.

Experiments on CUB-200-2011 and Stanford Cars datasets demonstrate that HyperPgNet outperforms other prototype learning architectures while using fewer parameters and training steps. Additionally, the concept-aligned HyperPg prototypes are learned transparently, enhancing model interpretability.

Primary Area: interpretability and explainable AI

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Submission Number: 3600