# Editable Image Generation with Consistent Unsupervised Disentanglement Based on GAN
## 4 Experiments
### Resistance Model Collapse
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**GAN** & **IS (Mean)** & **IS (Std)** & \(p\) & **IS (Ours Mean)** & **IS (Ours Std)** & \(p\) (Ours)** \\ \hline BECGAN [28] & 1.735 & 0.215 & \textless{}0.001 & **2.005** & 0.236 & \textless{}0.001 \\ EBGAN [10] & 1.666 & 0.224 & \textless{}0.001 & **1.733** & 0.244 & \textless{}0.001 \\ INFOGAN [32] & 1.563 & 0.197 & \textless{}0.001 & **1.677** & 0.208 & \textless{}0.001 \\ BGAN [36] & 1.822 & 0.203 & \textless{}0.001 & **2.932** & 0.221 & \textless{}0.001 \\ CGAN [31] & 1.978 & 0.209 & \textless{}0.001 & **2.065** & 0.235 & \textless{}0.001 \\ AAE [15] & **2.012** & 0.214 & \textless{}0.001 & 1.917 & 0.199 & \textless{}0.001 \\ ACGAN [16] & 1.771 & 0.231 & \textless{}0.001 & **1.871** & 0.238 & \textless{}0.001 \\ LSGAN [9] & 1.846 & 0.227 & \textless{}0.001 & **1.959** & 0.236 & \textless{}0.001 \\ SGAN [17] & 1.668 & 0.158 & \textless{}0.001 & **2.005** & 0.183 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Score (IS) test on GANs (Cifar-10). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**GAN** & **IS (Mean)** & **IS (Std)** & \(p\) & **IS (Ours Mean)** & **IS (Ours Std)** & \(p\) (Ours)** \\ \hline BECGAN [28] & 1.735 & 0.215 & \textless{}0.001 & **2.005** & 0.236 & \textless{}0.001 \\ EBGAN [10] & 1.666 & 0.224 & \textless{}0.001 & **1.733** & 0.244 & \textless{}0.001 \\ INFOGAN [32] & 1.563 & 0.197 & \textless{}0.001 & **1.677** & 0.208 & \textless{}0.001 \\ BGAN [36] & 1.822 & 0.203 & \textless{}0.001 & **2.932** & 0.221 & \textless{}0.001 \\ CGAN [31] & 1.978 & 0.209 & \textless{}0.001 & **2.065** & 0.235 & \textless{}0.001 \\ AAE [15] & **2.012** & 0.214 & \textless{}0.001 & 1.917 & 0.199 & \textless{}0.001 \\ ACGAN [16] & 1.771 & 0.231 & \textless{}0.001 & **1.871** & 0.238 & \textless{}0.001 \\ LSGAN [9] & 1.846 & 0.227 & \textless{}0.001 & **1.959** & 0.236 & \textless{}0.001 \\ SGAN [17] & 1.668 & 0.158 & \textless{}0.001 & **2.005** & 0.183 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Score (IS) test on GANs (MNIST). Figure 3: The results of the CGAN [31] and BGAN [36] model. In Figure 4, we compare the image generated in CelebA by the original DCGAN [33] and ours. We reduced the image size of CelebA to 96 \(\times\) 96 to speed up the training process for faster results. The generated image is also in the size of 96 \(\times\) 96. Compare our method with the method before improvement in Figure 4, it is not difficult to find that the image face generated by the original DCGAN [33] is blurry and distorted, and the hairstyle has fewer changes in shape and eyebrow shape. Figure 4b is generated by our method, which is improved on the original basis, it has better clarity and diversity. In Figure 4, the faces of the upper character (with a variety of hairstyles) in the (b) pictures are more clear, and the forehead hair of the task in the lower (b) pictures are from long to short, showing the diversity of hairstyles. The image generated by our method also has fewer facial artifacts than the images on the original DCGAN (a).