This paper aims to understand and improve the utility of the dropout operation from the perspective of game-theoretic interactions. We prove that dropout can suppress the strength of interactions between input variables of deep neural networks (DNNs). The theoretic proof is also verified by various experiments. Furthermore, we find that such interactions were strongly related to the over-fitting problem in deep learning. Thus, the utility of dropout can be regarded as decreasing interactions to alleviate the significance of over-fitting. Based on this understanding, we propose an interaction loss to further improve the utility of dropout. Experimental results have shown that the interaction loss can effectively improve the utility of dropout and boost the performance of DNNs.

中文翻译：

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从博弈论的角度解释和促进辍学

本文旨在从博弈论交互的角度理解和改进 dropout 操作的效用。我们证明 dropout 可以抑制深度神经网络 (DNN) 的输入变量之间的相互作用强度。理论证明也得到了各种实验的验证。此外，我们发现这种相互作用与深度学习中的过度拟合问题密切相关。因此，可以将 dropout 的效用视为减少交互以减轻过拟合的重要性。基于这种理解，我们提出了一种交互损失，以进一步提高 dropout 的效用。实验结果表明，交互损失可以有效提高 dropout 的效用并提升 DNN 的性能。