In recent years, convolutional neural networks (CNNs) are used in a large number of tasks in computer vision. One of them is object detection for autonomous driving. Although CNNs are used widely in many areas, what happens inside the network is still unexplained on many levels. Our goal is to determine the effect of Intrinsic dimension (i.e. minimum number of parameters required to represent data) in different layers on the accuracy of object detection network for augmented data sets. Our investigation determines that there is difference between the representation of normal and augmented data during feature extraction.

翻译：近年来,在计算机视觉领域,大量任务都使用进化神经网络(CNNs),其中之一是自动驾驶的物体探测。尽管有线电视新闻网在许多领域被广泛使用,但网络内部发生的情况在许多层面仍无法解释。我们的目标是确定内在层面(即代表数据所需的最低参数数目)在不同层面对扩大数据集的物体探测网络的准确性的影响。我们的调查确定,在特征提取过程中,正常数据与强化数据之间的代表性存在差异。