The Shapley value is widely regarded as a trustworthy attribution metric. However, when people use Shapley values to explain the attribution of input variables of a deep neural network (DNN), it usually requires a very high computational cost to approximate relatively accurate Shapley values in real-world applications. Therefore, we propose a novel network architecture, the HarsanyiNet, which makes inferences on the input sample and simultaneously computes the exact Shapley values of the input variables in a single forward propagation. The HarsanyiNet is designed on the theoretical foundation that the Shapley value can be reformulated as the redistribution of Harsanyi interactions encoded by the network.

翻译：Shapley值被广泛视为一种可信的归因度量指标。然而，当人们使用Shapley值解释深度神经网络输入变量的归因时，在实际应用中通常需要极高的计算成本才能近似获得相对准确的Shapley值。为此，我们提出一种新型网络架构HarsanyiNet，该网络在对输入样本进行推理的同时，通过单次前向传播即可精确计算输入变量的Shapley值。HarsanyiNet的设计理论基础在于：Shapley值可被重新表述为网络编码的Harsanyi交互作用的重新分配。