# Icon Generation Based on Generative Adversarial Networks
## 4 Discussion  
This study is an effective attempt at the intersection of icon design and deep learning. A large dataset consisting of 8 categories of icons is constructed, and StyleGAN is slightly improved and conditioned to generate icons by category. By comparing the IS and FID metrics with 105 visual Turing tests, the self-attention, spectral normalization, and relative discriminative distance operations added to the original model can improve the quality and diversity of the generated icons. Among them, the self-attention mechanism operation improves the IS most significantly, while the relative discriminative distance improves the FID most. We also visualize the icon generation process, and we can see that the model learns both color combinations and contour shapes. Notably, the model generates the first clear silhouette for the pose icons, which are considered to be of poor quality (about 5 epochs), while the house icons, which are of good quality, start to generate a clearer silhouette at the 28 th epoch.  
In addition, five evaluation indexes were defined according to Apple's icon design requirements and suggestions: legibility, simplicity, uniqueness, artistry and attraction, and 44 subjects with previous icon design experience were asked to evaluate the generated icons through a questionnaire. In the end, the three categories of expressions, weather and houses received higher scores, while the traffic and people's gestures categories received the lowest scores, which is consistent with the results of the visual Turing test. At the same time, the recognizability and simplicity of the generated icons were more recognized by the designers, and most of the UI designers who participated in the study thought that the generated icons could assist them in their daily icon design work. One interesting point in the study is that, compared with the weather and plant icons, which gave the subjects  
Figure 9: (**a**) Comparison of StyleGAN and ours evaluations; (**b**) Results of the survey on the usefulness of generating icons for design work.  
higher visual perception and evaluation, the quality of the generated icons was poorer than that of the character pose icons, but the participating designers generally thought that they could get more inspiration from the generated icons for character movement, thus improving their design efficiency and quality. Therefore, more abstract visual symbol generation can be studied in future research.  
Our study also has limitations: firstly, the data set we constructed in our study is only a small class of icons, and the quantity and quality of the training data are crucial for generation models like GAN. We believe that a major focus of future icon generation research is still on the construction of datasets, and it would be interesting to try to study other categories or styles of icons in addition to the 8 categories of face icons covered in our study. However, notably, in the process of icon collection, we found that the number of icons of other styles (linear, 2.5D icons) was significantly smaller compared to face icons, which would make it challenging for related generation studies. It is also shown in our study that when there is less commonality in the sample, it affects the quality of generated icons. Of course, with the rapid development of GANs on few-shot learning [44], it is believed that the generation of high-quality images on a small number of samples will gradually become mainstream in the future, which will undoubtedly give great help to icon generation studies. In addition, future research should be expanded on the applications related to the combination of icons and even interface design with GANs to make it more application-worthy, so that it could better assist designers in their work.