Version 1
: Received: 13 August 2024 / Approved: 13 August 2024 / Online: 13 August 2024 (13:07:20 CEST)
How to cite:
Yan, H.; Wang, Z.; Bo, S.; Zhao, Y.; Zhang, Y.; Lyu, R. Research on Image Generation Optimization based Deep Learning. Preprints2024, 2024080927. https://doi.org/10.20944/preprints202408.0927.v1
Yan, H.; Wang, Z.; Bo, S.; Zhao, Y.; Zhang, Y.; Lyu, R. Research on Image Generation Optimization based Deep Learning. Preprints 2024, 2024080927. https://doi.org/10.20944/preprints202408.0927.v1
Yan, H.; Wang, Z.; Bo, S.; Zhao, Y.; Zhang, Y.; Lyu, R. Research on Image Generation Optimization based Deep Learning. Preprints2024, 2024080927. https://doi.org/10.20944/preprints202408.0927.v1
APA Style
Yan, H., Wang, Z., Bo, S., Zhao, Y., Zhang, Y., & Lyu, R. (2024). Research on Image Generation Optimization based Deep Learning. Preprints. https://doi.org/10.20944/preprints202408.0927.v1
Chicago/Turabian Style
Yan, H., Yang Zhang and Ranran Lyu. 2024 "Research on Image Generation Optimization based Deep Learning" Preprints. https://doi.org/10.20944/preprints202408.0927.v1
Abstract
Image generation optimization is an important research direction in the field of deep learning, which aims to improve the performance of image generation models and the quality of generated images. In recent years, researchers have made significant progress in image generation optimization with the development of deep generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs). These models are able to generate high-quality, realistic images by learning the distribution of image data. In this study, a deep learning-based image generation optimization model was adopted, which combined the advantages of GAN and VAE. The model architecture consists of a generator and a discriminator, where the generator is responsible for generating the image and the discriminator is used to judge the authenticity of the image. In addition, the model also introduces attention mechanism and self-supervised learning strategy to further improve the quality and diversity of generated images. In the training process, a large-scale image dataset is used, and a variety of optimization algorithms are used to improve the stability and efficiency of the model. By evaluating various indicators of the generative model, including image quality, generation speed and model convergence, it was found that the introduced attention mechanism and self-supervised learning strategy significantly improved the performance of the model.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
