论文标题
通过合成数据减少对象探测器培训的现实世界数据的数量
Reducing the Amount of Real World Data for Object Detector Training with Synthetic Data
论文作者
论文摘要
许多研究已经调查了神经网络的培训，并通过合成数据为现实世界中的应用进行了研究。这项研究的目的是量化使用合成和现实世界数据的混合数据集时可以节省多少现实世界数据。通过对训练示例的数量和通过简单的功率定律进行检测性能之间的关系，我们发现对现实世界数据的需求可以减少多达70％，而无需牺牲检测性能。通过在现实世界数据集中使用不足的类别的类别，通过丰富混合数据集的培训，特别增强了对象检测网络的培训。结果表明，现实世界数据比率在5％至20％之间的混合数据集减少了对现实世界数据的需求，而无需降低检测性能。
A number of studies have investigated the training of neural networks with synthetic data for applications in the real world. The aim of this study is to quantify how much real world data can be saved when using a mixed dataset of synthetic and real world data. By modeling the relationship between the number of training examples and detection performance by a simple power law, we find that the need for real world data can be reduced by up to 70% without sacrificing detection performance. The training of object detection networks is especially enhanced by enriching the mixed dataset with classes underrepresented in the real world dataset. The results indicate that mixed datasets with real world data ratios between 5% and 20% reduce the need for real world data the most without reducing the detection performance.
