Guided Dropout

  • Rohit Keshari Indian Institute of Technology Delhi
  • Richa Singh Indian Institute of Technology Delhi
  • Mayank Vatsa Indian Institute of Technology Delhi

Abstract

Dropout is often used in deep neural networks to prevent over-fitting. Conventionally, dropout training invokes random drop of nodes from the hidden layers of a Neural Network. It is our hypothesis that a guided selection of nodes for intelligent dropout can lead to better generalization as compared to the traditional dropout. In this research, we propose “guided dropout” for training deep neural network which drop nodes by measuring the strength of each node. We also demonstrate that conventional dropout is a specific case of the proposed guided dropout. Experimental evaluation on multiple datasets including MNIST, CIFAR10, CIFAR100, SVHN, and Tiny ImageNet demonstrate the efficacy of the proposed guided dropout.

Published
2019-07-17