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arXiv · 2602.06613

DAVE: Distribution-aware Attribution via ViT Gradient Decomposition

Adam Wróbel·Siddhartha Gairola·Jacek Tabor·Bernt Schiele·Bartosz Zieliński·Dawid Rymarczyk
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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 \textit{(\underline{D}istribution-aware \underline{A}ttribution via \underline{V}iT Gradient D\underline{E}composition)}, 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.

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Adam Wróbel, Siddhartha Gairola, Jacek Tabor, Bernt Schiele, Bartosz Zieliński, Dawid Rymarczyk. 2026-02-06. DAVE: Distribution-aware Attribution via ViT Gradient Decomposition. https://arxiv.org/abs/2602.06613

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