  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.
