Deep learning technology has developed constantly and is applied in many fields. In order to correctly apply deep learning techniques, sufficient learning must be preceded. Various conditions are necessary for sufficient learning. One of the most important conditions is training data. Collecting sufficient training data is fundamental, because if the training data are insufficient, deep learning will not be done properly. Many types of training data are collected, but not all of them. So, we may have to collect them directly. Collecting takes a lot of time and hard work. To reduce this effort, the data augmentation method is used to increase the training data. Data augmentation has some common methods, but often requires different methods for specific data. For example, in order to recognize sign language, video data processed with openpose are used. In this paper, we propose a new data augmentation method for sign language data used for learning translation, and we expect to improve the learning performance, according to the proposed method.

中文翻译：

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用于人类关键点估计的数据增强基于深度学习的手语翻译

深度学习技术不断发展，并在许多领域得到应用。为了正确应用深度学习技术，必须先进行足够的学习。充分的学习需要各种条件。训练数据是最重要的条件之一。收集足够的培训数据至关重要，因为如果培训数据不足，则将无法正确进行深度学习。收集了许多类型的训练数据，但不是全部。因此，我们可能必须直接收集它们。收集需要大量时间和艰苦的工作。为了减少这种工作量，使用数据增强方法来增加训练数据。数据扩充有一些通用的方法，但是对于特定的数据通常需要不同的方法。例如，为了识别手语，使用经过openpose处理的视频数据。在本文中，我们提出了一种用于学习翻译的手语数据的新数据增强方法，并期望根据该方法提高学习性能。