Study of validation methods for augmented data

Jong-jin Jung, Kyung-Won Kim
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Abstract

This paper introduces a study to verify whether the expanded data through various data augmentation methods are valid in terms of accuracy and bias. Data augmentation is a method of processing and generating other types of data with similar characteristics based on the characteristics of the obtained data, rather than directly collecting data when there is not enough data to increase analysis accuracy. However, unverified and augmented data may actually degrade the results of the analysis. Before using the amplified data for analysis, it is a very important verification factor whether it is accurately propagated in terms of similarity to the source data, and whether bias occurs because only a specific part is concentrated and propagated as a result of the propagation. Therefore, in this paper, a verification method is presented from these two perspectives.
增强数据验证方法的研究
本文介绍了一项研究,以验证通过各种数据增强方法扩展的数据在准确性和偏倚方面是否有效。数据增强是指根据所获得数据的特征,处理和生成具有相似特征的其他类型数据的方法,而不是在数据不足时直接收集数据,以提高分析精度。然而,未经验证和扩充的数据实际上可能降低分析结果。在使用放大后的数据进行分析之前,从与源数据的相似度来看,是否得到了准确的传播,以及由于传播的结果只集中传播了特定的部分,是否产生了偏差,这是一个非常重要的验证因素。因此,本文从这两个角度提出了一种验证方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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