Optimization and Automatic Generation of Product Styling Process Design Based on Deep Learning

Abstract

This study presents a novel approach to optimizing and automating the product styling process design through the integration of deep learning techniques. Leveraging advanced neural network architectures, we developed a systematic methodology encompassing data preprocessing, model development, training, evaluation, and analysis to enhance creativity, efficiency, and competitiveness in the design workflow. Experimental results demonstrate the effectiveness of deep convolutional neural networks (CNNs) in accurately classifying design styles across diverse product categories, achieving high accuracy, precision, recall, and F1-score. Qualitative assessments by human evaluators further confirm the subjective quality and aesthetic appeal of the generated design outputs. The implications of this study extend beyond product styling process design, offering transformative opportunities for innovation and differentiation in industries reliant on design aesthetics for consumer engagement and brand identity. However, challenges such as the need for labeled datasets and concerns about model interpretability require careful consideration. Future research directions include exploring advanced deep learning techniques, integrating multimodal data sources, and fostering collaboration between human expertise and machine intelligence to unlock new frontiers of creativity and innovation. In summary, this study underscores the transformative potential of deep learning in revolutionizing the product styling process design and paving the way for a future where creativity and innovation thrive in harmony with technology.