DAVE: Distribution-aware Attribution via ViT Gradient Decomposition

Abstract

Vision Transformers (ViTs) have become a dominant architecture in computer vision, yet producing stable and high-resolution attribution maps for these models remains challenging. Architectural components such as patch embeddings and attention routing often introduce structured artifacts in pixel-level explanations, causing many existing methods to rely on coarse patch-level attributions. We introduce DAVE (Distribution-aware Attribution via ViT Gradient DEcomposition), a mathematically grounded attribution method for ViTs based on a structured decomposition of the input gradient. By exploiting architectural properties of ViTs, DAVE isolates locally equivariant and stable components of the effective input–output mapping. It separates these from architecture-induced artifacts and other sources of instability. Consequently, DAVE produces robust, precise and class-consistent attribution maps that reliably highlight visual features used by the model across inputs. Experimental results demonstrate that DAVE attributions are more stable and spatially precise than existing approaches.