Liang Jing
School of Fashion Media, Jiangxi Institute of Fashion Technology, Nanchang, 330000, China.
Sci Rep. 2024 Dec 28;14(1):31069. doi: 10.1038/s41598-024-82281-2.
This study proposes a novel artificial intelligence (AI)-assisted design model that combines Variational Autoencoders (VAE) with reinforcement learning (RL) to enhance innovation and efficiency in cultural and creative product design. By introducing AI-driven decision support, the model streamlines the design workflow and significantly improves design quality. The study establishes a comprehensive framework and applies the model to four distinct design tasks, with extensive experiments validating its performance. Key factors, including creativity, cultural adaptability, and practical application, are evaluated through structured surveys and expert feedback. The results reveal that the VAE + RL model surpasses alternative approaches across multiple criteria. Highlights include a user satisfaction rate of 95%, a Structural Similarity Index (SSIM) score of 0.92, model accuracy of 93%, and a loss reduction to 0.07. These findings confirm the model's superiority in generating high-quality designs and achieving high user satisfaction. Additionally, the model exhibits strong generalization capabilities and operational efficiency, offering valuable insights and data support for future advancements in cultural product design technology.
本研究提出了一种新颖的人工智能（AI）辅助设计模型，该模型将变分自编码器（VAE）与强化学习（RL）相结合，以提高文化创意产品设计的创新性和效率。通过引入人工智能驱动的决策支持，该模型简化了设计工作流程并显著提高了设计质量。该研究建立了一个全面的框架，并将该模型应用于四个不同的设计任务，通过广泛的实验验证了其性能。通过结构化调查和专家反馈对包括创造力、文化适应性和实际应用在内的关键因素进行了评估。结果表明，VAE+RL模型在多个标准上优于其他方法。亮点包括95%的用户满意度、0.92的结构相似性指数（SSIM）得分、93%的模型准确率以及将损失降低到0.07。这些发现证实了该模型在生成高质量设计和实现高用户满意度方面的优越性。此外，该模型具有强大的泛化能力和运营效率，为文化产品设计技术的未来发展提供了有价值的见解和数据支持。
