Mahyoub, Mohamed and Abdulhussain, Sadiq H. and Natalia, Friska and Sudirman, Sud and Mahmmod, Basheera M. (2023) Abstract Pattern Image Generation using Generative Adversarial Networks. In: 2023 15th International Conference on Developments in eSystems Engineering (DeSE).
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Abstract
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) |
|---|---|
| Keywords: | Abstract Pattern, Image Synthesis, Generative Adversarial Networks, Deep Convolutional GAN, Wasserstein GAN |
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods > 006.3 Artificial Intelligence, Machine Learning, Pattern Recognition, Data Mining |
| Divisions: | Faculty of Engineering & Informatics > Information System |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 07 Nov 2023 00:16 |
| Last Modified: | 07 Nov 2023 00:16 |
| URI: | https://kc.umn.ac.id/id/eprint/27048 |
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