# Icon Generation Based on Generative Adversarial Networks
## 1 Introduction  
Icons are an international language that can be observed ubiquitously in everyday life and are one of the most critical parts of a user interface (UI). It is a carrier between the user and the graphical interface, a mutual integration of function and aesthetics. A high-quality icon should not only enable users to quickly recognize and understand the meaning of the function it represents, but should also bring out the commercial character of the brand to some extent [1, 2]. The design team often needs to design different types of icons according to different application scenarios and customer needs, while the design process often requires significant communication and coordination between designers and customers. The design team needs to make the icon language-independent and provide quick navigation for users in different countries in an interactive interface of limited size, while it has to fit the brand concept and reflect the unique ethos of the brand's product. The drafting phase, where an icon is conceived and created, is considered to be the most time-consuming part of the entire design process, and this phase often requires the design team to invest extra time and a wealth of inspiration. The designer must ensure that the icon created expresses both the functionality of the application and its own uniqueness. In particular, the large number of new applications created every day and the graphic structure of the icon that fits the psychological metaphor of most people brings about the phenomenon of similarity in design (Figure 1), thus requiring designers to constantly adjust design details and strive for innovation in the icon design process to avoid too much similarity with icons of similar products. The use of artificial intelligence technology to assist in design and art creation has always been an interesting and meaningful research problem. With its superior computational and memory capabilities, artificial intelligence technology is able to provide the best design solutions through continuous accumulation of empirical knowledge and continuous optimization. Previous studies have proved that this technology has been successfully applied in the fields of apparel design [3], layout design [4], and circuit design [5]. With the success of Generative Adversarial Networks (GANs) [6] in image generation related tasks [7; 8; 9; 10], researchers have started to apply this technique to design, where GANs can learn from a large number of past design cases to find the regularities and eventually generate high-fidelity and diverse design solutions. For example, [PERSON] et al. proposed a GAN-based model to assist designers in chair design, which consists of an image synthesis module and a super-resolution module to generate high-quality chair design solutions [11]; [PERSON] et al. proposed a LayoutGAN that enables automatic page layout, and for a random set of input parameters, the model can output an improved set of parameters in a wireframe layout [12]; and HouseGAN++ [13] proposed by [PERSON] et al. enables the task of automatic design of house floor plans. GANs has also applied to icon design. The earliest related research could be traced back to [PERSON] et al. in 2017, where they first constructed a large-scale logo dataset containing 60w corporate logos, and gave the icons different labels by clustering methods to avoid the mode collapse that often occurs in GANs, and finally achieved the logo generation task [14]. [PERSON] et al. combined the ACGAN [15] with the WGAN-GP [16] to construct the LoGAN, which is based on the LLD-logo datasets [17] and eventually generated 12 types of logos with color as the label [18]. In 2019 this team constructed the LoGANv2, thus realizing the task of high-resolution icon generation, and the study was conducted on the StyleGAN [19] architecture. It experimented with conditional extensions and demonstrated that adding high-quality conditions to the unconditional model can effectively control the output of the synthetic network [20].