Issue |
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
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Article Number | 03006 | |
Number of page(s) | 7 | |
Section | Image Processing and Computer Vision | |
DOI | https://doi.org/10.1051/itmconf/20257003006 | |
Published online | 23 January 2025 |
Evaluation of Natural Image Generation and Reconstruction Capabilities Based on the β-VAE Model
Software Engineering, Nanchang Hangkong University, Nanchang, 330063, China
Corresponding author: 21201732@stu.nchu.edu.cn
Natural image generation models are crucial in computer vision. However, the Variational Autoencoder (VAE) has limitations in image quality and diversity, while β-VAE achieves a balance between the decoupling of latent space and generative quality by adjusting the coefficient β. This article evaluates the performance of β-VAE in natural image generation and reconstruction tasks, comparing it with Conditional VAE and Information VAE. Train the model on the CelebA dataset, using three metrics: Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Fréchet Inception Distance (FID) for matrix evaluation, to analyze the generation quality and reconstruction capability of each model. The results indicate that β-VAE performs well in reconstruction tasks, but due to the strong constraints of the latent space, the realism of the images is lower in generation tasks. Conditional VAE and Info VAE perform more balanced in terms of generation quality and task balance. This article suggests optimizing β-VAE by introducing adversarial training and flexible latent space modeling to enhance its generative capabilities. Future research can further validate the potential of β-VAE in different application scenarios.
© The Authors, published by EDP Sciences, 2025
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