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Research on the Principles and Applications of Generative Adversarial Networks in Image Style Transfer
- 1 College of Computer and Information Science, Southwest University School of Software, Southwest University, Chongqing 400715, China
* Author to whom correspondence should be addressed.
Abstract
With the rapid development of machine learning, the field of image style transfer has received widespread attention, and scientists have gradually realized that this is an important field in computer vision. Image style transfer refers to the conversion of an image from one visual style to another and preserving the content. The technology of changing image style has a wide range of applications in various fields from art to virtual reality. Although current learning models have achieved some results in the study of image style transfer, they still face challenges in improving resolution and complex style transfer. This paper mainly introduces the background and characteristics of existing learning models that can achieve image style transfer. It introduces the principles, characteristics and limitations of GANs. In the basis of introduction, this paper also analyses the differences and different characteristics of GANs and summarizes the advantages and disadvantages of GANs. At the same time, this paper predicts future research directions, continuing to optimize these models, reducing training costs, and exploring hybrid methods to further advance image style transfer technology. These developments not only strengthen the theoretical research of image style transfer and GANs, but also have broad application potential in fields such as art, design and medical imaging.
Keywords
image style transfer, GANs, deep learning model, computer vision.
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Cite this article
Qu,Y. (2024). Research on the Principles and Applications of Generative Adversarial Networks in Image Style Transfer. Applied and Computational Engineering,82,29-34.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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