OPAL: Orthonormal Prototype Alignment Learning for Interpretable Image Classification

Prototypical part-based models provide explainable predic-tions by comparing input regions to learned prototypes. However, currentapproaches are burdened by complex, multi-stage training pipelines andheavily rely on auxiliary regularization to prevent prototype collapse. Toovercome these limitations, we introduce Orthonormal Prototype Align-ment Learning (OPAL), a single-stage, end-to-end framework that sim-plifies interpretable classification. Our approach anchors the latent spaceusing predefined orthonormal bases, embedding each class within a dedi-cated subspace spanned by fixed part-prototypes. To achieve precise partlocalization, OPAL enforces spatial competition across feature maps.This mechanism isolates sparse, discriminative regions, directing eachprototype to consistently attend to the same semantic concept across dif-ferent images. By framing classification as a direct representation align-ment task, our method eliminates the need for auxiliary losses. Extensiveexperiments on fine-grained benchmarks demonstrate that OPAL outper-forms both its non-interpretable counterparts and state-of-the-art part-prototype methods, delivering granular visual explanations by explicitlyrevealing the specific image regions driving every prediction. Code isavailable at https://github.com/ilancarretero/OPAL.