Interpretable Vision Transformers in Image Classification via SVDA

2026-02-11

AI Summary

This work addresses the limited interpretability and lack of structural properties in the attention mechanism of Vision Transformers (ViT). It introduces, for the first time, a singular value decomposition (SVD)-inspired attention mechanism—termed SVDA—into ViT, leveraging a geometrically driven formulation to enhance attention sparsity, spectral structure, and interpretability. The proposed approach further incorporates interpretability metrics to dynamically monitor the training process. Evaluated on CIFAR-10, FashionMNIST, CIFAR-100, and ImageNet-100 benchmarks, the method achieves competitive classification accuracy while significantly improving the interpretability of attention maps. These results demonstrate a promising new direction for interpretable AI and the design of efficient, structured attention mechanisms in vision models.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Security and Privacy: Data transparency and provenance

Abstract

Vision Transformers (ViTs) have achieved state-of-the-art performance in image classification, yet their attention mechanisms often remain opaque and exhibit dense, non-structured behaviors. In this work, we adapt our previously proposed SVD-Inspired Attention (SVDA) mechanism to the ViT architecture, introducing a geometrically grounded formulation that enhances interpretability, sparsity, and spectral structure. We apply the use of interpretability indicators -- originally proposed with SVDA -- to monitor attention dynamics during training and assess structural properties of the learned representations. Experimental evaluations on four widely used benchmarks -- CIFAR-10, FashionMNIST, CIFAR-100, and ImageNet-100 -- demonstrate that SVDA consistently yields more interpretable attention patterns without sacrificing classification accuracy. While the current framework offers descriptive insights rather than prescriptive guidance, our results establish SVDA as a comprehensive and informative tool for analyzing and developing structured attention models in computer vision. This work lays the foundation for future advances in explainable AI, spectral diagnostics, and attention-based model compression.

Problem

• Vision Transformers
• interpretability
• attention mechanisms
• structured attention
• image classification

Innovation

• Vision Transformers
• Interpretable Attention
• SVD-Inspired Attention
• Spectral Structure
• Model Sparsity