Rademacher dropout: An adaptive dropout for deep neural network via optimizing generalization gap

Abstract

Dropout plays an important role in improving the generalization ability in deep learning. However, the empirical and fixed choice of dropout rates in traditional dropout strategies may increase the generalization gap, which is counter to one of the principle aims of dropout. To handle this problem, in this paper, we propose a novel dropout method. By the theoretical analysis of Dropout Rademacher Complexity, we first prove that the generalization gap of a deep model is bounded by a constraint function related to dropout rates. Meanwhile, we derive a closed form solution via optimizing the constraint function, which is a distribution estimation of dropout rates. Based on the closed form solution, a lightweight complexity algorithm called Rademacher Dropout (RadDropout) is presented to achieve the adaptive adjustment of dropout rates. Moreover, as a verification of the effectiveness of our proposed method, the extensive experimental results on benchmark datasets show that RadDropout achieves improvement of both convergence rate and prediction accuracy.