We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable image classifier that integrates the power of deep learning and the interpretability of case-based reasoning. This model classifies input images by comparing them with prototypes learned during training, yielding explanations in the form of "this looks like that." However, while previous methods use spatially rigid prototypes, we address this shortcoming by proposing spatially flexible prototypes. Each prototype is made up of several prototypical parts that adaptively change their relative spatial positions depending on the input image. Consequently, a Deformable ProtoPNet can explicitly capture pose variations and context, improving both model accuracy and the richness of explanations provided. Compared to other case-based interpretable models using prototypes, our approach achieves state-of-the-art accuracy and gives an explanation with greater context. The code is available at https://github.com/jdonnelly36/Deformable-ProtoPNet.

翻译：我们提出可变形原型部分网络（Deformable ProtoPNet），这是一种将深度学习能力与基于案例推理的可解释性相结合的可解释图像分类器。该模型通过将输入图像与训练过程中学习到的原型进行比较来进行分类，从而生成"此图像似彼图像"形式的解释。然而，此前方法使用空间刚性的原型，我们通过提出空间灵活的原型来弥补这一缺陷。每个原型由多个原型部分构成，这些部分会根据输入图像自适应地改变其相对空间位置。因此，可变形ProtoPNet能够显式捕获姿态变化与上下文信息，既提升了模型精度，又丰富了所提供的解释内容。与其他基于原型的可解释案例推理模型相比，我们的方法实现了最先进的准确率，并提供了具有更丰富上下文的解释。代码开源在 https://github.com/jdonnelly36/Deformable-ProtoPNet。