2 papers
cs.LG2026
An Overview of Prototype Formulations for Interpretable Deep Learning
Maximilian Xiling Li, Korbinian Franz Rudolf, Paul Mattes +2
Prototypical part networks offer interpretable alternatives to black-box deep learning models by learning visual prototypes for classification. This work provides a comprehensive a…
cs.CV2025
Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypes
Maximilian Xiling Li, Korbinian Rudolf, Nils Blank +1
Robotic agents need to understand how to interact with objects in their environment, both autonomously and during human-robot interactions. Affordance detection on 3D point clouds,…