# Guided Dropout

## TL;DR

This paper introduces guided dropout, a method that intelligently drops nodes based on their strength, leading to improved generalization in deep neural networks over traditional random dropout.

## Contribution

The paper proposes a novel guided dropout technique that measures node strength for dropout, unifying it with conventional dropout as a special case.

## Key findings

- Guided dropout outperforms traditional dropout on multiple datasets.
- Experimental results show improved generalization with guided dropout.
- Guided dropout effectively measures node importance for better training.

## Abstract

Dropout is often used in deep neural networks to prevent over-fitting. Conventionally, dropout training invokes \textit{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.