提出一种新方法，让ViT模型的解释更精准清晰。
DAVE: Distribution-aware Attribution via ViT Gradient Decomposition
- 基于视觉变换器结构分解梯度，分离出真实响应信号
- 消除分块嵌入和注意力路由带来的伪影干扰
- 适合需要高精度图像归因的研究者与应用
视觉变换器（ViTs）已成为计算机视觉的主流架构，但为其生成稳定且高分辨率的归因图仍具挑战性。分块嵌入和注意力路由等结构组件常在像素级解释中引入系统性伪影，导致现有方法多依赖粗糙的分块级归因。本文提出DAVE（Distribution-aware Attribution via ViT Gradient Decomposition），一种基于输入梯度结构化分解的数学严谨归因方法。通过利用ViT的架构特性，DAVE分离出有效输入-输出映射中的局部等变且稳定的成分，将其与架构引起的伪影及其他不稳定性来源区分开来。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。