[3D2-OS-12b] 創作者と人工知能が創る創作の未来(2/3)

[3D2-OS-12b-02] Data Augmentation for the Comic Dataset using TDGA AutoAugment

〇Wataru Yoshida¹, Akira Terauchi¹, Naoki Mori¹, Makoto Okada¹ (1. Osaka Prefecture University)

Keywords:Data Augmentation, TDGA AutoAugment, Understanding of Creations

Data Augmentation (DA) is a technique to generate additional data from existing data and effectively interpolate the data space. However, choosing the right DA for a given dataset and task requires a high level of expertise, a large amount of time, and in many cases a deep understanding of the data domain. In order to solve the above problems, with the recent development of automatic machine learning, attention has been paid to automatic DA application methods for image recognition tasks, and it has been reported that AutoAugment and Fast AutoAugment provide the best results in various image classification tasks. However, the conventional automatic application methods of DA have only dealt with benchmark problems and have not yet been applied to real-world data. Therefore, in this study, we investigate effective augmentation methods for cartoon data by using TDGA AutoAugment (TDGA AA), which is a method proposed by the authors to search for augmentation strategies that fit the problem while maintaining diversity. The effectiveness of the proposed TDGA AA is confirmed by computer simulation taking several cartoon datasets as examples.