Ising Dropout with Node Grouping for Training and Compression of Deep Neural Networks

Abstract

Dropout is a popular regularization method to reduce over-fitting while training deep neural networks and compress the inference model. In this paper, we propose Ising dropout with node grouping, which represents a deep multilayer perceptron (MLP) neural network as a graph with fixed grouped nodes and uses the Ising energy to drop group of nodes. This method is an extension to our proposed Ising dropout method, which had the limit of solving the Ising energy model for MLPs with limited graph order. The proposed fixed grouping method enables applying drop-out to deep MLPs with any order. Performance of this method is evaluated on handwritten digits (MNIST), Fashion-MNIST, Free Spoken Digit Dataset (FSDD), and Street View House Numbers (SVHN) datasets and compared with the standard dropout and standout methods. Preliminary results show that the proposed approach can keep the classification performance competitive to the original network while eliminating optimization of unnecessary network parameters in each training cycle. This method can compress the inference model significantly while maintaining the classification performance.