ProtoPVAE

Improving Prototype Consistency and Stability with Regularized Latent Spaces

Abstract

Prototype-based models aim to provide interpretability in image classification by representing categories through prototypical parts. However, existing approaches often suffer from unreliable prototypes, particularly with respect to consistency and stability. Consistency measures whether a prototype corresponds to the same semantic part across images of a class, while stability evaluates whether prototype activations remain aligned under input perturbations. Low performance on these criteria reduces the reliability of prototype-based explanations. While recent work has improved consistency and stability through additional architectural components or specialized loss terms, we explore an alternative approach. We extend the standard prototype-based framework by introducing a variational autoencoder (VAE) latent space after the feature extractor, while otherwise preserving the original ProtoPNet formulation. The VAE imposes a regularized latent representation that, when jointly optimized with the prototype-based objectives, promotes more stable and consistent prototype activations. Experiments on the CUB-200-2011 dataset show that the proposed model consistently improves prototype consistency and stability relative to most existing prototype-based methods across multiple backbone architectures, while maintaining competitive classification accuracy. Notably, these gains are achieved within the standard ProtoPNet framework and are comparable to methods that incorporate additional alignment mechanisms