Comparison of improved variational autoencoder models for human face generation

Abstract

The variational autoencoder (VAE) model has evolved a number of VAE improved models in the past 10 years, including CVAE, WAE, NVAE, etc. These models have greatly improved the calculation speed of VAE and the resolution of generated images. The main goal of this paper is to compare the principles of these different models and the effect of generating images. The principle analysis method is mainly used to study the improvement direction of the VAE model in different papers. The main ideas for improving the VAE model include optimizing the loss function, optimizing the objective function, introducing other parameters, or improving code efficiency. Some models even add many algorithms in computer vision in for improving image effect. In the experiments in this paper, results show the image results processed by different models. In the current optimal model NVAE, this algorithm has solved most of the VAE image blur problems, and has achieved perfection in image details. The improvement of the VAE model in the future may require innovative ideas. Under the current principle and model structure, the room for improvement of the VAE model is relatively limited. The final experiment shows that to improve the quality of VAE generated images, it is required to optimize the objective function, optimize the algorithm and add image visual processing, and then the generated images will be significantly improved.