论文标题
从游戏理论观点解释和增强辍学
Interpreting and Boosting Dropout from a Game-Theoretic View
论文作者
论文摘要
本文旨在从游戏理论互动的角度理解和改善辍学操作的实用性。我们证明，辍学可以抑制深神经网络（DNNS）输入变量之间相互作用的强度。理论证明还通过各种实验来验证。此外，我们发现这种相互作用与深度学习中的过度问题密切相关。因此，辍学的效用可以被视为减少相互作用以减轻过度拟合的重要性。基于这种理解，我们提出了一种交互损失，以进一步改善辍学的效用。实验结果表明，相互作用损失可以有效地改善辍学的效用并提高DNN的性能。
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.
