Application Research of Deep Learning in Clothing Style Design

This paper explores the application of deep learning in the realm of clothing style design, focusing on the development of a personalized clothing design system. The aim is to address diverse clothing consumption needs through the extraction of various excellent clothing styles and improving the level of personalized design. The article evaluates the proposed model's performance using external indicators, specifically clustering evaluation and normalized mutual information, from the perspective of deep learning. By utilizing deep learning techniques, the clothing style theme model introduced in this research is compared to traditional theme models. The evaluation results indicate a higher level of realism in the styles generated by the proposed deep learning-based model. The innovative approach achieves an impressive accuracy of up to 98.17%, showcasing the model's effectiveness in extracting clothing styling labels. This study not only contributes to the field of clothing style design but also underscores the potential of deep learning in enhancing the accuracy and realism of personalized design systems. The results suggest that integrating deep learning into clothing design processes can significantly elevate the capability to capture diverse and realistic clothing styles, marking a substantial advancement in the application of artificial intelligence in the fashion industry.