We show how solution concepts from cooperative game theory can be used to tackle the problem of pruning neural networks. The ever-growing size of deep neural networks (DNNs) increases their performance, but also their computational requirements. We introduce a method called Game Theory Assisted Pruning (GTAP), which reduces the neural network's size while preserving its predictive accuracy. GTAP is based on eliminating neurons in the network based on an estimation of their joint impact on the prediction quality through game theoretic solutions. Specifically, we use a power index akin to the Shapley value or Banzhaf index, tailored using a procedure similar to Dropout (commonly used to tackle overfitting problems in machine learning). Empirical evaluation of both feedforward networks and convolutional neural networks shows that this method outperforms existing approaches in the achieved tradeoff between the number of parameters and model accuracy.

使用合作博弈理论的解决概念来解决修剪神经网络的问题。引入了一种名为博弈理论辅助修剪（GTAP）的方法，通过游戏论解法根据神经网络中神经元对预测准确性的联合影响估计来减小网络的规模同时保持其预测准确性。经过实证评估，该方法在参数数量和模型准确性之间的权衡方面优于现有方法。

利用合作博弈理论修剪神经网络