Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization

TL;DR

This paper introduces advanced dropout, a model-free, adaptive dropout method optimized via variational Bayes, which significantly reduces overfitting and enhances performance across various deep learning tasks and datasets.

Contribution

It presents a novel, easily implementable dropout approach that adaptively adjusts dropout rates and extends to multiple applications, outperforming existing techniques.

Findings

Outperforms nine dropout techniques on seven datasets.

Achieves highest effectiveness ratios in most cases.

Effectively prevents overfitting and improves generalization.

Abstract

Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate overfitting and improve the performance of DNNs. The advanced dropout technique applies a model-free and easily implemented distribution with parametric prior, and adaptively adjusts dropout rate. Specifically, the distribution parameters are optimized by stochastic gradient variational Bayes in order to carry out an end-to-end training. We evaluate the effectiveness of the advanced dropout against nine dropout techniques on seven computer vision datasets (five small-scale datasets and two large-scale datasets) with various base models. The advanced dropout outperforms all the referred techniques on all the datasets.We further compare the effectiveness ratios and find that advanced dropout achieves the highest one on most cases. Next, we conduct a set of analysis of dropout rate characteristics, including convergence of the adaptive dropout rate, the learned distributions of dropout masks, and a comparison with dropout rate generation without an explicit distribution. In addition, the ability of overfitting prevention is evaluated and confirmed. Finally, we extend the application of the advanced dropout to uncertainty inference, network pruning, text classification, and regression. The proposed advanced dropout is also superior to the corresponding referred methods. Codes are available at https://github.com/PRIS-CV/AdvancedDropout.