# ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation \[Technical Report\] Source: [ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation \[Technical Report\]]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:31:26.317Z Authors: Hamed Ayoobi, Nico Potyka, Francesca Toni Published: 2023-11-26T21:52:47.000Z ## Abstract We propose ProtoArgNet, a novel interpretable deep neural architecture for image classification in the spirit of prototypical-part-learning as found, e.g., in ProtoPNet. While earlier approaches associate every class with multiple prototypical-parts, ProtoArgNet uses super-prototypes that combine prototypical-parts into a unified class representation. This is done by combining local activations of prototypes in an MLP-like manner, enabling the localization of prototypes and learning (non-linear) spatial relationships among them. By leveraging a form of argumentation, ProtoArgNet is capable of providing both supporting (i.e. \`this looks like that') and attacking (i.e. \`this differs from that') explanations. We demonstrate on several datasets that ProtoArgNet outperforms state-of-the-art prototypical-part-learning approaches. Moreover, the argumentation component in ProtoArgNet is customisable to the user's cognitive requirements by a process of sparsification, which leads to more compact explanations compared to state-of-the-art approaches. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()