OPAL: Orthonormal Prototype Alignment Learning for Interpretable Image Classification
Prototypical part-based models provide explainable predictions by comparing input regions to learned prototypes. However, current approaches are burdened by complex, multi-stage training pipelines and heavily rely on auxiliary regularization to prevent prototype collapse. To overcome these limitations, we introduce Orthonormal Prototype Alignment Learning (OPAL), a single-stage, end-to-end framework that simplifies interpretable classification. Our approach anchors the latent space using predefined orthonormal bases, embedding each class within a dedicated subspace spanned by fixed part-prototypes. To achieve precise part localization, OPAL enforces spatial competition across feature maps...
| Authors | Ilán Carretero, Gustavo Jesús Angulo, Rocío del Amor et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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