Analysis of the methods and performances for data augmentation: Image, text and audio

Abstract

With the rapid development in computation ability as well as machine learning scenarios, various artificial intelligence applications can be achieved in recent years. With this in mind, this study will explore the application of data augmentation in machine learning and deep learning. To be specific, this paper first introduces the background and research history of data augmentation and then discusses the research progress in recent years. The basic description of this study describes the definition, common methods, and evaluation metrics of data augmentation in detail. At the same time, three data augmentation models, AutoAugment, AugGPT, and SpecAugment++, are introduced respectively, including their principles, experimental results, as well as evaluation. Finally, according to the analysis, the limitations and prospects of the field are discussed and demonstrated, as well as summarize the main findings and research implications of the full paper. Overall, these results shed light on guiding further exploration of data augmentation.