Investigating the performance of Generative Adversarial Networks on Fabric Pattern Generation

Investigating the performance of Generative Adversarial Networks on Fabric Pattern Generation

Haarman, Luuk (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems)

Yildiz, B. (mentor)
van Gemert, J.C. (mentor)
Jonker, C.M. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2021-07-01

Generative adversarial networks (GANs) are a popular method for image generation, capable of state-of-the-art. Despite this popularity, the generation of fabric patterns remains somewhat unexplored. A possible reason for this could be that there is no publicly shared dataset large enough to train models. However, research into this topic could be relevant because designers can take up to multiple weeks for a single design, but a trained GAN could generate thousands of designs per day. Next to this fabric patterns have characteristics that make them different from the datasets commonly used right now, which means an investigation could yield new insights into the capabilities of GANs. This research investigated the performance of several GAN models on the task of fabric pattern generation and compared these to another popular method of image generation: variational autoencoders. By using open-source implementations of GAN models, and a dataset of fabric patterns provided by Vlisco, a Dutch fabric company, the performance of GANs has been evaluated. To protect the interests of Vlisco, this dataset cannot be shared. The results show clear promise in the capabilities of GANs to replicate these complex patterns, but these results are not yet of the same quality that GANs have been able to achieve in other domains. However, these results are an improvement from those of previous research. The results achieved by variational autoencoders are similar to those of GANs, but state-of-the-art GANs perform better. Due to limited time and resources, only low-resolution samples have been generated for evaluation. In the future, higher-resolution images could be generated which could yield additional insights into the current capabilities of GANs in this domain.

Generative Adversarial Network
Fabric Pattern Generation
Image Generation

http://resolver.tudelft.nl/uuid:9890dbd6-58aa-449e-b444-9566a47557f6

Student theses

bachelor thesis

© 2021 Luuk Haarman