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.

翻译：本文旨在从游戏理论互动的角度理解和改进辍学操作的效用。我们证明,辍学可以抑制深神经网络输入变量之间的相互作用强度。理论证据也由各种实验加以验证。此外,我们发现,这种相互作用与深知识的过度适应问题密切相关。因此,辍学的效用可被视为减少互动以降低过度适应的重要性。基于这一理解,我们提议进行互动损失,以进一步提高辍学的效用。实验结果表明,互动损失可以有效地改善辍学的效用,提高DNN的性能。