To better understand the behavior of image classifiers, it is useful to visualize the contribution of individual pixels to the model prediction. In this study, we propose a method, MoXI~($\textbf{Mo}$del e$\textbf{X}$planation by $\textbf{I}$nteractions), that efficiently and accurately identifies a group of pixels with high prediction confidence. The proposed method employs game-theoretic concepts, Shapley values and interactions, taking into account the effects of individual pixels and the cooperative influence of pixels on model confidence. Theoretical analysis and experiments demonstrate that our method better identifies the pixels that are highly contributing to the model outputs than widely-used visualization methods using Grad-CAM, Attention rollout, and Shapley value. While prior studies have suffered from the exponential computational cost in the computation of Shapley value and interactions, we show that this can be reduced to linear cost for our task.

翻译：为了更深入地理解图像分类器的行为，可视化单个像素对模型预测的贡献具有重要意义。本研究提出了一种名为MoXI（模型通过交互作用进行解释）的方法，该方法能高效准确地识别出具有高预测置信度的像素群组。该方法采用博弈论中的沙普利值和交互作用概念，综合考量了单个像素的影响以及像素间协同作用对模型置信度的贡献。理论分析与实验表明，与广泛使用的Grad-CAM、注意力回溯和沙普利值等可视化方法相比，我们的方法能更有效地识别对模型输出贡献显著的像素。尽管先前研究在计算沙普利值和交互作用时面临指数级计算成本的问题，但我们证明，针对本任务可将计算复杂度降低至线性级别。