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arXiv · 2304.01811

HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation

Lu Chen·Siyu Lou·Keyan Zhang·Jin Huang·Quanshi Zhang
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Abstract

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.

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Lu Chen, Siyu Lou, Keyan Zhang, Jin Huang, Quanshi Zhang. 2023-04-04. HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation. https://arxiv.org/abs/2304.01811

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