# Interpretable Vision Transformers in Image Classification via SVDA Source: [Interpretable Vision Transformers in Image Classification via SVDA]() Collection index: [llms.txt]() Data updated: 2026-02-13T05:00:15.212Z Authors: Vasileios Arampatzakis, George Pavlidis, Nikolaos Mitianoudis, Nikos Papamarkos Published: 2026-02-11T16:20:32.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()