# ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery

Source: [ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery](<https://www.sota2.com/research/paper/68fa6d711e209131cea39ed6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:55.238Z

Authors: Dawid Rymarczyk, {\\L}ukasz Struski, Jacek Tabor, Bartosz Zieli\\'nski

Published: 2020-11-29T11:23:05.000Z

## Abstract

In this paper, we introduce ProtoPShare, a self-explained method that incorporates the paradigm of prototypical parts to explain its predictions. The main novelty of the ProtoPShare is its ability to efficiently share prototypical parts between the classes thanks to our data-dependent merge-pruning. Moreover, the prototypes are more consistent and the model is more robust to image perturbations than the state of the art method ProtoPNet. We verify our findings on two datasets, the CUB-200-2011 and the Stanford Cars.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2011.14340>)

[PDF](<https://arxiv.org/pdf/2011.14340.pdf>)