Abstract
Prototypical-part methods, e.g., ProtoPNet, enhance interpretability in image
recognition by linking predictions to training prototypes, thereby offering
intuitive insights into their decision-making. Existing methods, which rely on
a point-based learning of prototypes, typically face two critical issues: 1)
the learned prototypes have limited representation power and are not suitable
to detect Out-of-Distribution (OoD) inputs, reducing their decision
trustworthiness; and 2) the necessary projection of the learned prototypes back
into the space of training images causes a drastic degradation in the
predictive performance. Furthermore, current prototype learning adopts an
aggressive approach that considers only the most active object parts during
training, while overlooking sub-salient object regions which still hold crucial
classification information. In this paper, we present a new generative paradigm
to learn prototype distributions, termed as Mixture of Gaussian-distributed
Prototypes (MGProto). The distribution of prototypes from MGProto enables both
interpretable image classification and trustworthy recognition of OoD inputs.
The optimisation of MGProto naturally projects the learned prototype
distributions back into the training image space, thereby addressing the
performance degradation caused by prototype projection. Additionally, we
develop a novel and effective prototype mining strategy that considers not only
the most active but also sub-salient object parts. To promote model
compactness, we further propose to prune MGProto by removing prototypes with
low importance priors. Experiments on CUB-200-2011, Stanford Cars, Stanford
Dogs, and Oxford-IIIT Pets datasets show that MGProto achieves state-of-the-art
image recognition and OoD detection performances, while providing encouraging
interpretability results.