CAMs作为基于Shapley值的解释器
CAMs as Shapley Value-based Explainers
AI总结：
提出CRG框架和ShapleyCAM方法，通过合作博弈与二阶泰勒展开提供理论扎实的CAM解释，并在ImageNet上验证其有效性。
摘要：
类激活映射（CAM）方法被广泛用于可视化神经网络的决策，但其底层机制仍未完全理解。为了增强对CAM方法的理解并提高其可解释性，我们引入了内容保留博弈论（CRG）解释器。该理论框架通过将神经网络预测过程建模为合作博弈，阐明了GradCAM和HiResCAM的理论基础。在此框架内，我们开发了ShapleyCAM，一种利用梯度和Hessian矩阵提供更精确且理论扎实的视觉解释的新方法。由于精确Shapley值计算在计算上不可行，ShapleyCAM采用合作博弈效用函数的二阶泰勒展开来推导闭式表达式。此外，我们提出了残差Softmax目标类（ReST）效用函数，以解决softmax前和softmax后分数的局限性。在ImageNet验证集上对12个流行网络的大量实验证明了ShapleyCAM及其变体的有效性。我们的发现不仅推进了CAM的可解释性，还弥合了启发式驱动的CAM方法与计算密集型的基于Shapley值的方法之间的差距。代码可在\url{https://github.com/caihuaiguang/pytorch-shapley-cam}获取。
英文摘要：
Class Activation Mapping (CAM) methods are widely used to visualize neural network decisions, yet their underlying mechanisms remain incompletely understood. To enhance the understanding of CAM methods and improve their explainability, we introduce the Content Reserved Game-theoretic (CRG) Explainer. This theoretical framework clarifies the theoretical foundations of GradCAM and HiResCAM by modeling the neural network prediction process as a cooperative game. Within this framework, we develop ShapleyCAM, a new method that leverages gradients and the Hessian matrix to provide more precise and theoretically grounded visual explanations. Due to the computational infeasibility of exact Shapley value calculation, ShapleyCAM employs a second-order Taylor expansion of the cooperative game's utility function to derive a closed-form expression. Additionally, we propose the Residual Softmax Target-Class (ReST) utility function to address the limitations of pre-softmax and post-softmax scores. Extensive experiments across 12 popular networks on the ImageNet validation set demonstrate the effectiveness of ShapleyCAM and its variants. Our findings not only advance CAM explainability but also bridge the gap between heuristic-driven CAM methods and compute-intensive Shapley value-based methods. The code is available at \url{https://github.com/caihuaiguang/pytorch-shapley-cam}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
