# Big Data and AI-Driven Product Design: A Survey
## 4 Product Design Based on Big Data and AI
### Product Design Based on Image Data
GAN and its extended models have made significant contributions to product design but the generated images often suffer from blurriness, lack of detail, and low quality. To address these problems, [PERSON] et al. [184] proposed Design-GAN, which introduces a texture similarity constraint mechanism. Similarly, [PERSON] et al. [5] combined GAN with topology optimization to enhance the quality of two-dimensional wheel images. By limiting the wheel image to a non-design domain, a pre-defined domain, and a design domain for topology optimization, they achieved results that were significantly different from the initial design. Neural style transfer is a deep generative model that enables the generation of images by separating and reconstructing their content and style. Nevertheless, neural style transfer is not simply a matter of overlapping a content image with a style image. Its implementation relies to the features learned by CNN. Figure 13 shows the idea of neural style transfer. In 2015, [PERSON] et al. [210] found that the image style and content could be separated in CNN and manipulated independently. Building upon this finding, they proposed a neural style transfer algorithm to transfer the style of famous artworks. Later, [PERSON] and [PERSON] [211] introduced an adaptive instance normalization (AdaIN) layer, which aligns the mean and variance of content features with style features to realize arbitrary style transfer. Inspired by their method, our previous research [212] combined Kansei engineering with neural style transfer to generate product design schemes (Figure 11c), resulting in an enhanced semantic of the generated product. Additionally, we developed a real-time design system that allowed users to input images and receive product schemes as output [193]. In a similar vein, [PERSON] et al. [213] combined GAN with neural style transfer to generate fashionable Dunhuang clothes, using GAN to generate clothing shapes and neural style transfer to add Dunhuang elements. Neural style transfer is performed over the entire image, limiting its flexibility. By using masks, it is possible to transfer different styles to different parts of a product [211]. For example, using masks, three style images can be transferred to the collar, pocket, and sleeve of a coat, respectively. Overall, neural style transfer still offers tremendous untapped potential for product design. Although GAN and its extended models show some limitations in generating high-quality and controllable designs due to their strong randomness, recent work by [PERSON] et al. [214] have demonstrated that AI and GAN can improve customer satisfaction and product design freshness, indicating their potential in generating innovative and attractive design proposals. On the other hand, neural style transfer is a technique used for  
Figure 13: Neural style transfer. Setting z as the random noise, G (z) is the synthetic image generated by the generator (G). The pre-trained VGG 19 is used to calculate the style loss between G (z) and the style image (S), the content loss between G (z) and the content image (C). Minimize the total loss that consists of style and content loss to optimize G. Once the loss goal is reached, G (z) will be output and marked as O. O can preserve the content feature of C and the style feature of S.  
generating product schemes with good image quality, strong interpretability, and controllability. While it may lack the innovation ability of GAN, it can be regarded as a combination of design elements such as product shape, texture, and color. Generation using these models promises to be a cost-effective way to generate product designs and support designers in creating design schemes quickly. However, further research is necessary to enhance the controllability and quality of GAN-based design generation and explore the potential of combining these techniques to generate innovative and high-quality design schemes. Generally, image data and AIGC have broken old thinking of traditional product design.