论文标题
数据质量对深度神经网络培训的影响
Impact of Data Quality on Deep Neural Network Training
论文作者
论文摘要
众所周知，数据对于培训神经网络至关重要。关于训练网络所需的数据数量的数量已经写成。但是，关于数据质量如何影响此类网络的融合，没有太多出版物。缺乏有关良好数据的信息（对于任务）。这项经验实验研究探讨了数据质量的一些影响。本文中显示了具体结果，简单变化如何影响平均平均精度（地图）。
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).
