Interpreting and Boosting Dropout from a Game-Theoretic View

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