ProtoMappingNet: Interpretable Hierarchical Prototypes through Relational Prototype Mappings

Interpretable machine learning aims to develop models whoseinternal reasoning is transparent and aligned with human perception.While prototype-based networks provide intuitive part-based interpreta-tions, most existing methods rely on flat or independently learned proto-type sets, failing to capture the hierarchical and relational organization ofvisual concepts. Inspired by theories of structured part-whole perception,we introduce ProtoMappingNet, a prototype-based architecture thatlearns hierarchically structured visual concepts through learnable cross-layer prototype mappings. These mappings align representations acrosstexture, part, and object levels, enabling coherent parent-child relationsbetween prototypes. To quantitatively evaluate the learned hierarchy,we propose a relational evaluation framework consisting of HierarchicalRelational Consistency (HRC) and Hierarchical Spatial Stability (HSS),which measure cross-level relational consistency and spatial containmentwithout requiring part annotations. Experiments show that ProtoMap-pingNet produces coherent multi-level explanations and substantially im-proves structural consistency through bidirectional relational modelingwhile maintaining competitive classification accuracy. Our code is avail-able at https://github.com/pk9403/ProtoMappingNet