# Editable Image Generation with Consistent Unsupervised Disentanglement Based on GAN
## 5 Conclusions  
In this work, we introduced a new method to train generative networks. We apply our method to generative adversarial training by learning disentanglement representations. We train the generator to optimize this disentanglement representation through loss or hyperparameters. Our framework accumulates many desirable properties: it does not  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Edges + Shoes**} & \multicolumn{3}{c}{**CLEVR-Simple**} & \multicolumn{3}{c}{**CLEVR-Complex**} & \multicolumn{3}{c}{**CLEVR-U**} \\ \cline{2-13}  & **PPL (\(\downarrow\))** & **FID (\(\downarrow\))** & **VP (\(\uparrow\))** & **PPL** & **FID** & **VP** & **PPL** & **FID** & **VP** & **PPL** & **FID** & **VP** \\ \hline InfoGAN [32] & 2952.2 & **10.4** & 15.6 & 56.2 & **2.9** & 28.7 & 83.9 & **4.2** & 27.9 & 766.7 & 3.6 & 40.1 \\ ProGAN [46] & 3154.1 & 10.8 & 15.5 & 64.5 & 3.8 & 27.2 & 84.4 & 5.5 & 25.5 & 697.7 & 3.4 & 40.2 \\ SeFa [12] & 3154.1 & 10.8 & 24.1 & 64.5 & 3.8 & 58.4 & 84.4 & 5.5 & 30.9 & 697.7 & 3.4 & 42.0 \\ Hessian Penalty [13] & 554.1 & 17.3 & 28.6 & 39.7 & 6.1 & 71.3 & 74.7 & 7.1 & 42.9 & 61.6 & 26.8 & 79.2 \\ OrJoRa [14] & **236.7** & 16.1 & 32.3 & **6.7** & 4.9 & 76.9 & **10.4** & 10.7 & 48.8 & **40.9** & 4.6 & **90.7** \\ Ours & 336.79 & 12.5 & **33.7** & 27.6 & 3.3 & **79.1** & 36.3 & 4.3 & **49.3** & **44.1** & **3.3** & 88.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different methods on Edge + Shoes and CLEVR.  
Figure 10: Comparison of rotation disentanglement of the CelebA-HQ dataset by our method, Hessian penalty [13], and OroJaR [14].  
require any additional trainable parameters, it operates in an unsupervised environment, and it outperforms state-of-the-art methods in some image datasets. Moreover, our method exhibits stable adversarial training, and our method allows embedding any variant of the GAN model. In the future, the neural radiance field can be combined with existing models to achieve a better disentanglement representation.  
Conceptualization, Y.Q., G.Y. and X.F.; methodology, Y.Q.; software, X.F.; validation, Y.Q., G.Y. and X.F.; formal analysis, Y.Q. and G.Y.; investigation, Y.Q. and G.Y.; resources, X.F.; data curation, G.Y.; writing--original draft preparation, Y.Q. and G.Y.; writing--review and editing, X.F.; visualization, G.Y. and X.F.; supervision, X.F.; project administration, Y.Q.; funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The School Foundation of Anhui University of Science and Technology grant number No:2021 CX2102.  
Not applicable.  
The code is available in [[https://github.com/mathinabel/HessianAndJobian](https://github.com/mathinabel/HessianAndJobian)]([https://github.com/mathinabel/HessianAndJobian](https://github.com/mathinabel/HessianAndJobian)), accessed on 26 April 2022.  
This research has been supported by the School Foundation of Anhui University of Science and Technology.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GAN & Generative Adversarial Networks \\ D & Discriminator \\ G & Generator \\ KL & Kullback-Leibler divergence \\ FID & Frechet Inception Distance \\ IS & Inception Score \\ \end{tabular}