Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes

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