Impact of Data Quality on Deep Neural Network Training

TL;DR

This paper investigates how data quality influences the convergence and performance of deep neural networks, highlighting that simple data quality improvements can significantly affect metrics like mean average precision.

Contribution

It provides empirical evidence on the effects of data quality on neural network training and convergence, an area with limited prior research.

Findings

Data quality impacts neural network convergence.

Simple data improvements can enhance mean average precision.

Empirical results demonstrate the importance of data quality in training outcomes.

Abstract

It is well known that data is critical for training neural networks. Lot have been written about quantities of data required to train networks well. However, there is not much publications on how data quality effects convergence of such networks. There is dearth of information on what is considered good data ( for the task ). This empirical experimental study explores some impacts of data quality. Specific results are shown in the paper how simple changes can have impact on Mean Average Precision (mAP).