| id |
cdrf2021_69 |
| authors |
Virginia Ellyn Melnyk |
| year |
2021 |
| title |
Punch Card Patterns Designed with GAN |
| source |
Proceedings of the 2021 DigitalFUTURES
The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021) |
| doi |
https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_7
|
| summary |
Knitting punch cards codify different stitch patterns into binary patterns, telling the machine when to change color or to generate different stitch types. This research utilizes Neural Networks (NN) and image-based Generative Adversarial Networks (GAN), with an image database of knitting punch cards, to generate new punch card designs. The hypothesis is that artificial intelligence will learn the basic underlying structures of the punch cards and the pattern makeup that is inherent across patterns of different styles and cultures. Different neural networks were utilized throughout the research, such as Neural Style Transfer (NST), AdaIN Style Transfers, and StyleGAN2. The results from these explorations offer different insights into pattern design and various outcomes of the different neural networks. Ultimately physically testing these punch card designs, these patterns were knit on a domestic knitting machine, resulting in novel fabrication and design techniques that are both digital and craft-based. |
| series |
cdrf |
| email |
|
| full text |
 file.pdf (3,781,792 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/09/29 07:53 |
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