## 2 Related Studies  
Existing data augmentation studies are divisible into two approaches. The first approach is based on model-based algorithms. [PERSON] et al. [26] proposed an algorithm that synthesizes virtual 3D object images superimposed on specific background images. [PERSON] et al. [27] proposed an image generation method from unknown viewpoints using structural features extracted from 3D objects. They applied their method to data augmentation for creating super-high-resolution images combined with 2D to 3D transformations. [PERSON] et al. [28] proposed a data augmentation method for pedestrian detection and pose estimation. They specifically controlled the poses and appearances of the existing data. However, these data augmentation methods were applied to unsupervised classification tasks because the augmentation targets were merely source data without labels. As one example of research into automatic label generation, [PERSON] et al. [29] detected pedestrians using labels generated from outdoor scene images that were created using a simulator. Their proposed domain-adapted pedestrian classifier was trained by combining a small number of pedestrian samples from a real-world target domain with numerous ones from a virtual-world source domain. [PERSON] et al. [30] proposed a method to synthesize images and labels in a virtual urban environment. They demonstrated application experiments for semantic segmentation using synthesized multi-camera images from a virtual car after annotation, including different labels for each season. The second approach is based on learning algorithms. This approach includes numerous methods based on conventional machine-learning (CMT) algorithms and DL algorithms using state-of-the-art backbones such as ConvNets [31; 32; 33; 34; 35; 36; 37; 38; 39] and Transformers [40; 41; 42; 43; 44]. Recently, numerous studies of GANs have been conducted for data generation and augmentation [45; 46; 47; 48; 49]. Fundamentally, GANs comprise a generator and a discriminator. A generator supplies virtual images generated from noise signals to a discriminator. The learning objective for a discriminator is the classification of real images and generated pseudo-images. The learning objective for a generator is the opposite of that of a discriminator. This relation is consistent with the mini-max principle [50]. A discriminator learns from both images while avoiding false segmentation of augmented images provided by a generator. The respective networks modify weights simultaneously and in parallel to opposite objectives as adversarial learning. [PERSON] et al. [51] proposed a deep convolutional GAN (DCGAN) for unsupervised representation learning. They introduced convolutional layers as an architectural topology inside of the generator. Based on the energy-based GAN (EBGAN) [52], [PERSON] et al. [53] proposed a boundary equilibrium GAN (BEGAN) that uses a convolutional auto-encoder (CAE) [54] for a discriminator. Although DCGAN contained no mechanism to judge the convergence of learning for a generator and a discriminator, BEGAN solved this difficulty after introducing loss functions to the respective networks. [PERSON] et al. [55] proposed a progressive growing GAN (PGGAN) that adds new layers according to learning progress. They demonstrated not only the creation of high-resolution images, but also a reduction in computational cost for training because their proposed method gradually learned the overall image structure. As an alternative generator architecture based on the arbitrary style transfer mechanism [56], [PERSON] et al. [57] proposed StyleGAN for learning unsupervised separation of high-level attributes and stochastic variation in the augmented images. They introduced not only a learning mechanism to increase resolutions resembling PGGAN progressively, but also local vectors to add probabilistic variation and global vectors to decide the object orientations and respective parts in the generative process.