Impact of Data Quality on Deep Neural Network Training
This addresses a gap in understanding data quality's role in neural network convergence, though it appears incremental.
The study investigates how data quality affects deep neural network training, showing that simple changes can impact Mean Average Precision (mAP).
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).