Abstract: In view of the problems that traditional real-time style transmission technology requires a large number of sample map training, low image quality, lack of realism and detail, this study combines the improved generative adversarial network (GANs) with real-time style transfer technology, and enhances the real-time style transfer calculation with adaptive instance normalization. As a result, a novel intelligent clothing ethnic pattern design model is developed. Experimental results show that the model reduces physical memory usage by 45.7%, with only 453MB, and utilizes only 26% of CPU resources in terms of CPU usage. The training time is approximately 20 minutes and 48 seconds. This model performance is obviously higher than other models. The designed intelligent clothing ethnic pattern design model in this study demonstrates higher clarity and shorter processing time, and has potential applications in the field of image generation.

Yingjun Liu and Ming Wu, “Intelligent Design of Ethnic Patterns in Clothing using Improved DCGAN for Real-Time Style Transfer” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01411105

@article{Liu2023,
title = {Intelligent Design of Ethnic Patterns in Clothing using Improved DCGAN for Real-Time Style Transfer},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01411105},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01411105},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {11},
author = {Yingjun Liu and Ming Wu}
}