Data augmentation is important for training neural networks, especially when there is not enough data to train a network well. However, data augmentation that results in the loss of label information may reduce the performance of the model. Most conventional data augmentation methods have been developed for image- or sound-related tasks, in which case the label information of the augmented data is easily and intuitively verified by human observation. However, in the case of sensor signals, it is difficult to recognize whether there is a change in the label information of the augmented data. We propose a systematic data augmentation method to maximize the performance by automatically finding the range of augmentation that preserves the labels of the augmented data. The experimental results show that the proposed method to extract the label-preserving range is practical and that the retrained model using data augmented within this range improves the performance by at least 5% without the need to further optimize the model architecture.

中文翻译：

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移动传感器数据的标签保留数据增强

数据增强对于训练神经网络很重要，尤其是当没有足够的数据来很好地训练网络时。然而，导致标签信息丢失的数据增强可能会降低模型的性能。大多数传统的数据增强方法都是为图像或声音相关的任务开发的，在这种情况下，增强数据的标签信息可以通过人类观察轻松直观地验证。然而，在传感器信号的情况下，很难识别增强数据的标签信息是否有变化。我们提出了一种系统的数据增强方法，通过自动查找保留增强数据标签的增强范围来最大化性能。