The Art of Machine Learning as Fashion Stylish for Designing Clothes

Abstract

Over the years, designers have come to the fore with their originality and personal styles and have shaped the fashion industry with their designs. However, due to the progress of time, designers have become unable to meet the demands of all consumers. Since it takes a lot of time to produce an original design, the production process progresses slowly, and customers are uncomfortable with this situation. As in many other industries, designers are trying to solve this problem with the help of artificial intelligence, which is indispensable in the fields of commerce, art, and security. It first entered the fashion sector with drawing programs in the 1950s and has started to change the fashion sector since the 2000s. In the 1950s, artificial intelligence was used only to create a virtual drawing environment When the designer makes a mistake, he can simply erase the mistake and continue working on the design without having to start the whole design from scratch. These programs have greatly facilitated the work of designers. Designers are now able to draw their designs in a much shorter time. But even this shortened period is not enough for the whole fashion industry. Designers could still not keep up with the demands of all customers. Thanks to researchers who added different perspectives to artificial intelligence in the early 2000s with its usage not only for drawing but also for designing, Therefore, in this paper, it is aimed at producing some original designs by preserving the designer's style with the use of Aí techniques. With the proposed model, it has become able to produce ready-made designs by using features such as object detection and visual processing. The experimental results showed that Aí techniques are very successful for combining different patterns for producing an original fashion style.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 5 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.