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.
翻译:我们引入了可解释的图像分类模型Proto Pool,这是一个由各班级共享的原型库;培训比现有方法更为简单,因为它不需要修剪阶段;培训是通过对特定类的原型进行完全不同的分配获得的;此外,我们引入了一种新的相似的焦点功能,将模型的重点放在稀有的地表特征上;我们显示,Protopool获得了CUB-200-2011和斯坦福汽车数据集的最新准确性,大大减少了原型的数量。我们提供了对方法的理论分析和用户研究,以表明我们的原型比以竞争性方法获得的原型更独特。