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.

ProtoPool 是一种可解释的图像分类模型，共享类别的原型池，并采用完全可微分的方法将原型分配给特定的类别，同时还引入了新颖的聚焦相似函数，将模型集中于罕见的前景特征，它在 CUB-200-2011 和 Stanford Cars 数据集上取得了最先进的准确性，同时大大减少了原型的数量。