Interpretable Image Classification with Differentiable Prototypes Assignment

• URL: http://arxiv.org/abs/2112.02902v1
• Date: Mon, 6 Dec 2021 10:03:32 GMT
• Title: Interpretable Image Classification with Differentiable Prototypes Assignment
• Authors: Dawid Rymarczyk, {\L}ukasz Struski, Micha{\l} G\'orszczak, Koryna Lewandowska, Jacek Tabor, Bartosz Zieli\'nski
• Abstract summary: We introduce ProtoPool, an interpretable image classification model with a pool of prototypes shared by the classes. It is obtained by introducing a fully differentiable assignment of prototypes to particular classes. We show that ProtoPool obtains state-of-the-art accuracy on the CUB-200-2011 and the Stanford Cars datasets, substantially reducing the number of prototypes.
• Score: 7.660883761395447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce ProtoPool, an interpretable image classification model with a pool of prototypes shared by the classes. The training is more straightforward than in the existing methods because it does not require the pruning stage. It is obtained by introducing a fully differentiable assignment of prototypes to particular classes. Moreover, we introduce a novel focal similarity function to focus the model on the rare foreground features. We show that ProtoPool obtains state-of-the-art accuracy on the CUB-200-2011 and the Stanford Cars datasets, substantially reducing the number of prototypes. We provide a theoretical analysis of the method and a user study to show that our prototypes are more distinctive than those obtained with competitive methods.

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