Research on the Application of Variational Autoencoder in Image Generation

Abstract

The rapid development of artificial intelligence and deep learning has significantly influenced the domain of image creation, finding extensive applications in applications in fields like medical imaging, computer vision, and entertainment. Despite these advancements, challenges remain, especially in enhancing the quality and variety of produced images. This paper concentrates on applying Variational Autoencoders (VAEs) to image generation, a topic of increasing importance due to the model’s theoretical interpretability and stability. Through a detailed analysis of VAE principles, architecture, and applications, this research underscores the model’s capabilities in producing high-quality, varied images and its effectiveness in tasks such as image denoising and enhancement. The study also analysis the limitations of VAEs, like the inclination to generate blurry images, and discusses potential improvements, including hybrid models and enhanced loss functions. The results of this research enhance the comprehension of VAE’s capabilities and provide a foundation for future research aimed at advancing image generation technologies.