# Big Data and AI-Driven Product Design: A Survey
## 4 Product Design Based on Big Data and AI
### Product Design Based on Image Data
The model combines image features extracted by CNN with handcrafted features and can help users understand their preferred styles. Most products do not stand alone, and their compatibility with other products must be considered during the design process. To address this, [PERSON] et al. [175] used Siamese networks to assess the style compatibility between pairs of furniture images. They also proposed a joint visual-text embedding model for recommendation, based on furniture type, color, and material. [PERSON] et al. [176] built a graph neural network (GNN) model to account for multiple items' interactions, instead of just pairwise compatibility. Their GNN model comprised a deep CNN to extract image features and enabled evaluating the compatibility of multiple furniture items based on style, color, material, and overall appearance. Moreover, they applied the GNN model to solve the fill-in-the-blank task, for example, recommending the most suitable bed from multiple alternatives based on a given desk, cabinet, chair, and mirror. _Generate new product images._ In the field of product design, two popular models are used to generate new product images: GAN and neural style transfer. Both of these models take images as input and produce output images. GAN is an unsupervised model that consists of two neural networks, namely the generator and discriminator [177]. The generator creates images, while the discriminator evaluates the generated images against real images. Both networks are trained simultaneously and are improve through competition. Figure 10 shows the structure of GAN. We summarize the contribution of GAN to product design as follows: schemes generation [178, 179, 180, 151], text-to-image synthesis [181], generative transformation [182], collocation generation [183, 184, 185], sketch acquisition [152, 186], colorization [187, 188, 190], and virtual display [28, 191, 192]. Some examples of new product generation based on image data are shown in Figure 11. In Figure 11a, a new image of handbag is generated given an image from the shoe. In Figure 11b, the edge image is input, new shoes are generated in the second and fourth columns, and the rest are ground truth. In Figure 11c, a product image and style image are inputted, then a new product will be generated. The automatic generation of product schemes is made possible by using a large number of existing product images. GAN can learn the distribution of product features from these images and generate new designs through the gradual optimization of a generator by a discriminator. For example, [PERSON] et al. [151] used GAN to create new smartwatches schemes by training their model with 5459 smartwatch images. They also compared the results of GAN with various extensions, including deep convolution GAN (DCGAN), least-squares GAN (LSGAN), and Wasserstein GAN (WGAN). In another example, to produce collocation clothing images, [PERSON] et al. [194] proposed Attribute-GAN, which comprises a generator and two discriminators. However, GAN only generate one view of the product, which is insufficient for design purposes. To address this limitation, [PERSON] et al. [191] proposed conditional variational GAN (CVGAN), which can synthesize arbitrary product views. This is useful for virtual display, especially for designs that require high shape and appearance standards, such as clothing design where it's necessary to show the effect of try-on. Moreover, GAN and its extended models are also applicable to layout generation, such as interior, websites, and advertisements [6; 195; 196]. Text-to-image synthesis is a promising technique for product design, although current research in this field is scarce. One notable study by [PERSON] et al. [181] proposed an enhanced attentional GAN (e-AttnGAN) for generating fashion images from text descriptions, such as \"long sleeve shirt with red check pattern\". Most existing studies aim to improve the synthesis technology [197; 198; 199; 200], and the potential of text-to-image synthesis has yet to be fully realized by designers.