###### Abstract
Training GANs requires constructing appropriate loss function, evaluation indicators, and constraints to ensure the stability and effectiveness of the training, which is extremely difficult and resource-consuming. Compared with color, object, artistic style, and other attributes, the aesthetics of the image have strong uncertainty and subjectivity, and there is no basis for completely qualitative aesthetics; therefore; it is difficult to design and add appropriate aesthetic constraints for GANs to generate images with a better aesthetic effect. * The current datasets used to train the aesthetic conditions of GANs are small, and the quality of the generated image content semantics is poor. The aesthetic condition training set of the GANs must meet the requirements of both semantic and aesthetic labels, and no large-scale dataset currently meets this requirement. Even with pre-training on larger datasets, the image semantics are less effective compared to those of the existing GANs trained on large-scale datasets. To give a better aesthetic presentation effect to images generated by GANs, we proposed a method called global aesthetic image generation algorithm based on GAN's disentangled representation. It makes use of its ability to reconstruct high-quality image content and mine the global aesthetic disentangled representation in latent space to generate images with high overall aesthetic quality and realistic content. Since the algorithm is based on  
Figure 1: A comparison of the generated images and the images taken artificially. the existing aesthetic image generation algorithm of GANs, there is no need to design the corresponding aesthetic loss and aesthetic constraints for the generation of an aesthetic image, and it is not necessary to redesign and train the generation model, which greatly saves on training time and training resources. Existing aesthetic image generation models require large-scale datasets with semantic labels and aesthetic labels. The disentangled representation learning-based aesthetic GAN that we proposed differs from existing aesthetic image generation algorithms in many ways. First, our algorithm directly utilizes an existing GAN model for semantic image reconstruction, and on this basis, obtains both semantic and aesthetic images through aesthetic editing of the latent space. However, with existing aesthetic image generation algorithms, there is a need to design aesthetic losses and aesthetic constraints to train the conditional GAN models, which is very time-consuming and labor-intensive. Second, our algorithm does not need to be trained on a large-scale dataset with both semantic and aesthetic labels like an aesthetically conditioned GAN; we only need to train a prediction model on a dataset with only aesthetic labels, and then a support vector machine decision surface is learned based on the initial generated image and its aesthetic score. The desired image, with global aesthetics, can be obtained through the aesthetic editing of the latent space of the image. In summary, this study makes the following contributions:  
* We creatively apply the decoupled representation learning of GAN to aesthetic image generation. By learning the global aesthetic disentangled representation of the latent space, mining the aesthetic representation in the latent space of the StyleGAN model, and using the aesthetic representation learned to edit the latent space properly, we generate images with higher aesthetic quality. * An aesthetic image generation algorithm based on an existing GAN model is designed to multiplex the high-quality reconstruction ability of the existing GAN model on the image content and add global aesthetic information to its latent space to make the generated image both aesthetic and semantic. * By training the global aesthetic feeling prediction model to learn the global aesthetic feeling disentangled representation of GAN, the generation of images with more global aesthetic feeling is realized.