LGOct 11, 2024
An Overview of Prototype Formulations for Interpretable Deep LearningMaximilian Xiling Li, Korbinian Franz Rudolf, Nils Blank et al.
Prototypical part networks offer interpretable alternatives to black-box deep learning models. However, many of these networks rely on Euclidean prototypes, which may limit their flexibility. This work provides a comprehensive overview of various prototype formulations. Experiments conducted on the CUB-200-2011, Stanford Cars, and Oxford Flowers datasets demonstrate the effectiveness and versatility of these different formulations.