# Editable Image Generation with Consistent Unsupervised Disentanglement Based on GAN
## 4 Experiments
### Disentanglement Experiments  
#### 4.3.1 Qualitative Experiments  
In this subsection, we compare Hessian penalty [13] and OroJaR [14] with our method. After adding the \(\lambda_{1}\) and \(\lambda_{2}\) parameters in the model training, some cases will bring more serious attribute entanglement, so we add the Equation (15) to disentanglement and compare the current best results. Figure 5 shows experiments been done on fox, balloons, and Tibetan mastiffs on ImageNet. We moved the images in Z-space for each row. Z-space represents the sample space of latent variables.The input position of the Z-space refers to Figure 1. Through the change of the Z-space parameters, the model generates a series of pictures, as shown in Figure 5. The role of these diagrams is explained next. The first line in Figure 5 shows the enlargement of the fox's face, after disentanglement, the Z-space parameters can control the distance between the object and the lens; the second line shows the separation and change of the background, we separate the balloon from the background, separate the balloon property, and change the background without changing this property, and the third line shows the separation and action of the foreground object (the Tibetan mastiff gradually stands up). The foreground object is a Tibetan mastiff, and the background is grass. By selecting the direction of the Z-space, the foreground object can make actions without changing the background. Figure 4: Graph generated without our Network(DCGAN [33]) (**a**), with ours (**b**). \begin{table}
\begin{tabular}{c c c} \hline
**GAN** & **FID (Original)** & **FID (Ours)** \\ \hline BGAN [36] & 243.633 & 226.164 \\ BEGAN [28] & 417.625 & 377.760 \\ CGAN [31] & 382.941 & 373.581 \\ AAE [15] & 187.612 & 181.169 \\ ACGAN [16] & **231.143** & 249.174 \\ LSGAN [9] & 149.675 & 133.783 \\ SGAN [17] & 147.770 & **130.251** \\ \hline \end{tabular}
\end{table} 
Table 3: Score (FID) test on GANs (Cifar-10). In Figure 6, we present the experimental results of our model on the Edge + Shoes dataset. We choose the two interpretable dimensions of shoe color and style to display, with each row corresponding to one interpretable dimension. Figure 6 shows OroJaR [14], Hessian penalty [13], and our method learned the two separated changes, i.e., style and shape of shoes (the first row of each sub-image is generated by the Hessian method [13], the middle row by the OroJaR [14] method, and the last row is produced by our method). Subfigure (a) shows the performance of different methods in disentangling shape entanglement, and our method covers more shapes. Subfigure (b) shows different methods in disentangling color in terms of entanglement performance; Hessian penalizes [13] to generate a single color of the image, and OroJaR [14] generates a serious color coupling of shoe images. Our method covers more colors, and the independence of colors is better than others. Figure 7 shows the results on the CLEVR simple dataset. Our method is able to separate the potential space effectively. We show the four scoring dimensions of \(x\) axial shift, \(y\) axial shift, color, and shape. Hessian penalty learning independently controls the vertical and horizontal axial position of the object (first row of each subplot of Figure 7). However, the shape is not controlled during the vertical movement. OroJaR [14] successfully separates the horizontal, shape factors in CLEVR (second row of each subfigure of Figure 7), but shows a tendency to shift toward the horizontal in the separation of the vertical movement. Lastly, our method can successfully separate independent axial shifts and colors in CLEVR (the third row of each subplot of Figure 7). Figure 5: The orthogonal orientation learned on BigGAN [2]. Figure 6: Comparison of the disentanglement quality of OroJaR [14], Hessian penalty [13] and our method for Edge + Shoes. Figure 8 shows the comparison on the CLEVR complex dataset. Here we show the relative position as a factor.