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ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery

2020-11-29 11:23:05
Dawid Rymarczyk, Łukasz Struski, Jacek Tabor, Bartosz Zieliński
arXiv_AI
       
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.

Abstract (translated)

URL

https://arxiv.org/abs/2011.14340

PDF

https://arxiv.org/pdf/2011.14340.pdf

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