## 1 Introduction
The Mixup is an approach that uses probabilistic distributions based on existing data. It involves randomly selecting data pairs from the existing dataset. In the Mixup, sampling is executed according to the beta distribution. Fast auto augment (Fast AA) and population-based augmentation (PBA) also approach sampling data based on existing data according to augmentation policies [9; 10]. The second category comprises deep learning-based data augmentation techniques, such as transfer learning [11] and synthesized data generated by deep generative neural networks (DGNNs) (e.g., the generative adversarial network (GAN) [12; 13] and variational autoencoder (VAE) network [14]). Aside from effectively augmenting data, data augmentation methods based on synthesized data generated by DGNNs are effective for security applications [15; 16]. The security issue is significant in the analysis of survey research on small sample sizes, including rare populations. Respondents in a survey search are limited, and the characteristics of their population might be easily speculated; hence, invaders can easily specify their individual information. Thus, data augmentation approaches using synthesized data by DGNNs are promising approaches. Therefore, in this study, we focus on a data augmentation technique using synthesized data generated by DGNNs. The effectiveness of data augmentation techniques using synthesized data generated by DGNNs has been confirmed for a wide variety of computer vision tasks [7; 11]. These techniques have been modified and extensively applied to tabular data forms. When addressing a small-sample-sized data issue, such as an imbalanced data problem, oversampling methods are usually used to augment the small data. The synthetic minority oversampling technique (SMOTE) is a primary helpful approach for oversampling small sample-sized data in tabular data, and modifications have been developed (i.e., adaptive synthetic sampling and borderline SMOTE) [17]. This approach's base method augments minority class data by interpolating data based on the distance between minority and majority data. The usefulness of these oversampling approaches has prevailed in various areas, such as business and physiological data [18; 19]. Although these oversampling methods play a crucial role in solving small-sample-sized data problems, we focus on DGNN-based augmentation techniques as data augmentation methods for tabular data, not to address an imbalanced data problem. DGNN-based augmentation techniques for the tabular data format may be appropriate for addressing issues related to small-sized consumer survey data because consumer survey data are generally organized into a tabular data format. Several promising algorithms have been developed. Medical GAN (medGAN) is the approach of applying data synthesized by DGNNs to patient data [20]. Table GAN (TGAN) [21], which modifies the deep convolutional GAN [13] for tabular-formed data, is a universal data augmentation approach that is not limited to specified purposes. Conditional table GAN (CTGAN) modifies the TGAN to enable imbalanced data, while the tabular variational autoencoder (TVAE) is an adjusted VAE for the tabular data format [22]. CTGAN and TVAE are not limited to specified purposes, similar to TGAN. Moreover, [PERSON] et al. [17] demonstrated that CTGAN and TVAE outperform other GAN-based synthesizers. They are likely to be effective for this study because they are effective for augmenting tabular-format data in various fields besides consumer survey data [23; 24; 25]. Generally, most tabular-formed data comprise multiple types of data formats, such as numerical and categorical data. CTGAN and TVAE are modified conventional GAN-based techniques for mixed table-formed data [22; 26]. However, they did not consider the dependencies between variables. Several studies have reported that GAN preprocessed by a copula function, known as CopulaGAN, is an appropriate technique for synthesizing data with dependencies among variables [27; 28].