## 1 Introduction  
In the last few years, deep neural networks (DNNs) have been successfully applied to a range of computer vision tasks, including classification [1; 2; 3], detection [4; 5; 6], segmentation [7; 8], and information fusion [9; 10]. However, because data augmentation is essential for the effective training of DNNs, and because there are numerous image-to-image translation and information fusion problems that need to be overcome, deep generative models have received significant attention. In this field, research on facial datasets has been particularly active, because they have a large number of real-world applications, such as facial classification and the opening of closed eyes in photos. Despite this increase in research interest, implementing generative models remains challenging because the process required to generate realistic images from low-level to high-level information is complex. Since [PERSON] et al. [11] first proposed the generative adversarial network (GAN), which is based on adversarial learning between two networks, a generator and a discriminator, many GAN models have demonstrated excellent performance in terms of their photo-realistic output. The key principle underlying the use of a GAN is to ensure that the probability distribution of the generated data is close to that of the real data via the adversarial training of the generator and discriminator. In the early stages of training, the generator may generate poor-quality images; thus, the discriminator can easily distinguish between real and fake samples. As the generator learns more during training, its output becomes more photo-realistic and the discriminator finds it more difficult to distinguish between real and fake samples. When the training reaches convergence, the generator can generate realistic but fake images. However, many GAN models suffer from instability during the training process, leading to problems such as mode collapse and the lack of diversity. BEGAN [12] is an auto-encoder-based GAN model with an auto-encoder architecture as the discriminator. Unlike many existing GAN models [11; 13; 14] that attempt to directly match the real data distribution, this model seeks to match the loss distribution of the auto-encoder. The BEGAN developers introduced an equilibrium hyperparameter to maintain the balance between the generator and the discriminator. It makes it possible for a user to control the visual quality and diversity of an image by changing the parameters. However, it suffers from the trade-off between diversity and quality, is subject to mode collapse, and occasionally fails to generate high-quality images during the training phase. StyleGAN [15] can generate photo-realistic output images using a style-based generator that considers the scale-specific characteristics of the generated image. Each layer in the StyleGAN generator consists of several convolutional layers and adaptive instance normalization (AdaIN) [16] layers. The AdaIN layers utilize latent vectors as input and then utilize their information with an affine transform. In addition, StyleGAN can perform style mixing, in which an image generated using two different latent vectors has both characteristics. However, StyleGAN generates an image from noise; thus, it cannot mix two existing images; i.e., it does not take existing images as input. Synthesizing two existing images using the model requires the training of an additional network that can encode real images into the latent space of StyleGAN. Motivated by StyleGAN, we propose a generator that takes two latent vectors as input based on the scale-specific role of each layer in the generator. The front layers are involved in the creation of high-level features such as the overall shape of the face, while the back layers are involved in lower-level features such as hair color and the microstructure. Our discriminator is trained to reconstruct only real images, and its decoder has the same structure as the generator.