In this work, we introduce an extension to ProtoPNet called ProtoPShare which shares
prototypical parts between classes. To obtain prototype sharing we prune prototypical parts …

Existing prototypical-based models address the black-box nature of deep learning.
However, they are sub-optimal as they often assume separate prototypes for each class …

Image recognition with prototypes is considered an interpretable alternative for black box
deep learning models. Classification depends on the extent to which a test image “looks …

Prototypical part network (ProtoPNet) methods have been designed to achieve interpretable
classification by associating predictions with a set of training prototypes, which we refer to as …

Interpretable methods based on prototypical patches recognize various components in an
image in order to explain their reasoning to humans. However, existing prototype-based …

Vision models are interpretable when they classify objects on the basis of features that a
person can directly understand. Recently, methods relying on visual feature prototypes have …

When we are faced with challenging image classification tasks, we often explain our
reasoning by dissecting the image, and pointing out prototypical aspects of one class or …

Global interpretability is a vital requirement for image classification applications. Existing
interpretability methods mainly explain a model behavior by identifying salient image …

Humans usually explain their reasoning (eg classification) by dissecting the image and
pointing out the evidence from these parts to the concepts in their minds. Inspired by this …

We argue that there are many notions of'similarity'and that models, like humans, should be
able to adapt to these dynamically. This contrasts with most representation learning …