OPAL: Orthonormal Prototype Alignment Learning for Interpretable Image Classification

2026-08-30

AI Summary

为解决原型部分模型训练复杂及依赖辅助正则化问题，提出OPAL方法，通过预定义正交基简化分类过程，并在细粒度基准测试中表现优异。

Abstract

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. This mechanism isolates sparse, discriminative regions, directing each prototype to consistently attend to the same semantic concept across different images. By framing classification as a direct representation alignment task, our method eliminates the need for auxiliary losses. Extensive experiments on fine-grained benchmarks demonstrate that OPAL outperforms both its non-interpretable counterparts and state-of-the-art part-prototype methods, delivering granular visual explanations by explicitly revealing the specific image regions driving every prediction. Code is available at https://github.com/ilancarretero/OPAL.

Problem

• prototype collapse
• interpretable classification
• multi-stage training

Innovation

• Orthonormal Prototype Alignment Learning
• interpretable image classification
• spatial competition
• end-to-end framework
• representation alignment