Generating African inspired fashion designs
| dc.contributor.author | Malobola, Lindiwe | |
| dc.date.accessioned | 2023-11-14T15:12:10Z | |
| dc.date.available | 2023-11-14T15:12:10Z | |
| dc.date.issued | 2022 | |
| dc.description | A dissertation submitted in fulfilment of the requirements for the degree of Master of Science to the Faculty of Science, University of the Witwatersrand, Johannesburg, 2022 | |
| dc.description.abstract | Fashion has drawn a lot of attention from researchers in computer vision in recent years with a growing number of papers and workshops dedicated to this topic. There has been rapid development in fashion-related work ranging from retail sales forecasting [76, 79, 20], fashion trends analysis [10, 81, 31], fashion synthesis and recommendation [39, 8]. Fashion trends analysis often involves identifying patterns and predicting future fashion demands based on cities, season and runway fashion. Fashion image synthesis involves using generative models such as Generative Adversarial Networks (GANs) [27] and Variational Autoencoders (VAEs) [42] to generate new samples of fashion images. Fashion recommendation focuses on recommending clothing pieces or outfits given some conditions such as users’ preferences, occasion and weather conditions. | |
| dc.description.librarian | PC(2023) | |
| dc.faculty | Faculty of Science | |
| dc.identifier.uri | https://hdl.handle.net/10539/36990 | |
| dc.language.iso | en | |
| dc.school | Computer Science and Applied Mathematics | |
| dc.subject | Fashion Designs | |
| dc.subject | Seshwshwe traditional wear | |
| dc.title | Generating African inspired fashion designs | |
| dc.type | Dissertation |