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Abstract Pattern Image Generation using Generative Adversarial Networks

Mahyoub, M, Abdulhussain, SH, Natalia, F, Sudirman, S and Mahmmod, BM (2023) Abstract Pattern Image Generation using Generative Adversarial Networks. In: Proceedings - 2023 15 International Conference on Developments in eSystems Engineering, (DESE) , 2023-J. pp. 172-177. (2023 15 International Conference on Developments in eSystems Engineering, (DESE), 09-12 January 2023, Baghdad & Anbar, Iraq).

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Abstract pattern is very commonly used in the textile and fashion industry. Pattern design is an area where designers need to come up with new and attractive patterns every day. It is very difficult to find employees with a sufficient creative mindset and the necessary skills to come up with new unseen attractive designs. Therefore, it would be ideal to identify a process that would allow for these patterns to be generated on their own with little to no human interaction. This can be achieved using deep learning models and techniques. One of the most recent and promising tools to solve this type of problem is Generative Adversarial Networks (GANs). In this paper, we investigate the suitability of GAN in producing abstract patterns. We achieve this by generating abstract design patterns using the two most popular GANs, namely Deep Convolutional GAN and Wasserstein GAN. By identifying the best-performing model after training using hyperparameter optimization and generating some output patterns we show that Wasserstein GAN is superior to Deep Convolutional GAN.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
T Technology > TS Manufactures
Divisions: Computer Science & Mathematics
Publisher: IEEE
SWORD Depositor: A Symplectic
Date Deposited: 12 Jun 2023 16:48
Last Modified: 12 Jun 2023 16:48
DOI or ID number: 10.1109/DeSE58274.2023.10099871
URI: https://researchonline.ljmu.ac.uk/id/eprint/19758
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