# Icon Generation Based on Generative Adversarial Networks
## 3 Experiments and Results
### User Study
#### 3.7.2 Questionnaire Survey  
We selected 24 design graduate students and 20 UI designers to conduct a questionnaire study on the quality of the generated icons. The survey was conducted offline, and all participants had experience in designing visual symbols such as icons or logos. The UI designers were mainly from the UX Lab of Samsung Electronics China Research & Development Center in Nanjing. We combined the icon design requirements and suggestions for developers from Apple's official website [2], and selected 5 subjective evaluation criteria for icon quality: legibility, simplicity, uniqueness, aristry and attraction. We asked the subjects to evaluate the generated 8 categories of icons (10 generated icons of each category were randomly selected) based on the aforementioned five criteria (Table 4). In addition, we also set a question at the end of the questionnaire: 'How helpful do you think the generated icons in this study are to your daily icon design work?'. All the aforementioned questions were asked using 5-point Richter scale points. The 5-point scale is expressed as '1 = very low, 2 = little low, 3 = moderate, 4 = little high, 5 = very high.' Generated icons scored higher on average on the recognizability and simplicity metrics than on others, with legibility being rated highest and artistry being lowest. As with the results of the visual Turing test, the traffic and human gesture icons scored relatively lower than the other categories of icons. We believe that the reason for this phenomenon is that there are fewer commonalities in these two icon categories-specifically, the traffic category \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Legibility**} & \multicolumn{2}{c}{**Simplicity**} & \multicolumn{2}{c}{**Uniqueness**} & \multicolumn{2}{c}{**Artistry**} & \multicolumn{2}{c}{**Attraction**} \\ \cline{2-11}  & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** & **Mean** & **Std.** \\ \hline Weather & 4.31 & 1.06 & 3.93 & 0.86 & 3.29 & 1.51 & 3.04 & 1.29 & 3.23 & 1.39 \\ Emotion & 3.41. & 1.09 & 3.56 & 1.15 & 2.75 & 0.95 & 2.40 & 1.02 & 2.60 & 1.23 \\ Cloths & 3.52 & 0.81 & 2.36 & 1.04 & 2.95 & 0.85 & 2.20 & 1.11 & 2.27 & 1.14 \\ Plants & 3.54 & 1.05 & 3.11 & 0.75 & 2.50 & 1.19 & 2.90 & 1.53 & 2.65 & 1.36 \\ House & 3.80 & 1.09 & 3.18 & 0.83 & 2.27 & 1.23 & 2.54 & 1.19 & 2.38 & 1.15 \\ Avatar & 3.97 & 0.78 & 3.59 & 0.49 & 3.00 & 0.43 & 2.25 & 1.13 & 2.40 & 1.19 \\ Human & 3.75 & 1.22 & 3.02 & 0.83 & 3.09 & 0.70 & 1.95 & 0.82 & 2.90 & 1.32 \\ Transport & 2.90 & 0.79 & 2.43 & 1.07 & 2.23 & 1.22 & 1.72 & 0.78 & 1.97 & 0.72 \\ _Average_ & **3.78** & 1.05 & 3.12 & 0.88 & 2.76 & 1.13 & **2.38** & 1.21 & 2.55 & 1.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative metrics of our model from the user perceptual study. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **Weather** & **Emotion** & **Clothes** & **Plants** & **House** & **Avatar** & **Human** & **Transport** & **Total** \\ \hline StyleGAN & 86.3\% & 83.5\% & 97.4\% & 88.9\% & 83.4\% & 99.2\% & 94.5\% & 100\% & 91.6\% \\ Ours & 68.4\% & 77.0\% & 87.5\% & 76.2\% & 71.7\% & 75.6\% & 89.1\% & 97.1\% & 80.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The visual Turing test accuracy of 8 types of icons. contains more categories, such as trucks, motorcycles, cars, etc., while the human gesture category is more challenging to generate due to the rich diversity of human actions. We conducted the aforementioned research on the results generated by StyleGAN in the same environment, and the comparison between the results and our final model is shown in Figure 9a: we can see that our model has the most obvious improvement for uniqueness and legibility, while the improvement effect of artistry and attractiveness is weaker, so overall the improved model has better generation results.