## 1 Introduction
The lack of defective datasets results in poor model training, which requires a model with strong generalization ability that can adapt to changes in different scenarios and maintain high detection accuracy and robustness. However, in practical applications, it is difficult to ensure good generalization ability of the model due to the quality and quantity of the dataset as well as the structure and parameters of the model, which leads to false detection or missed detection when the model is faced with new or unknown defects [13]. One of the key challenges for deep learning models of industrial defect detection is how to obtain diverse and high-quality industrial sample datasets. Adversarial Generative Networks (GAN) is an innovative and influential technique in deep learning, which consist of two neural networks: a generator and a discriminator. They use adversarial training to build unsupervised learning models that can learn and generate datasets with similar distribution characteristics to the training data without relying on prior information. The generator tries to produce samples that are indistinguishable from the real target samples, while the discriminator tries to distinguish between the generated samples and the real samples. The two networks compete with each other until they reach a Nash equilibrium. Currently, GAN and their derived models have gradually become a research hotspot in the field of data generation, mainly involving two research directions: models based on network architecture and loss function and models based on domain crossing. Models based on network architecture and loss function mainly improve the objective function and network structure of the original GAN to solve problems such as unstable training, mode collapse, etc., and improve the quality and diversity of the generated samples. The generator tries to minimize the discrimination error of the discriminator for its generated samples. The cGAN [14] proposed a GAN based on conditional probability distribution, which can input additional labels, texts, or image information as conditions to the generator and discriminator and improve the realism of the generated samples according to this information. The discriminator tries to maximize its ability to distinguish between real samples and generated samples. AC-GAN [15] proposed a GAN based on an auxiliary classifier, which adds an extra classifier in the discriminator to predict the category of the input sample, and combines the classification loss and discrimination loss to optimize the network. This can improve the quality and diversity of the generated samples and also use category information to control the generation process. The objective function of the original GAN is a minimax game, which has problems such as gradient vanishing, saddle point, KL divergence asymmetry, etc. Therefore, WGAN introduced Wasserstein distance as a measure between the real distribution and the generated distribution and gave a simple and effective algorithm to optimize this distance. WGAN [16] can avoid gradient vanishing and mode collapse and provide a meaningful indicator of training progress. Models based on domain crossing mainly use the relationship or transformation rules between different domains to achieve cross-domain generation tasks. In the original GAN design, the generator can only map from a random noise space to a data space and cannot achieve transformation between different data spaces. Pix2 Pix [17] proposed a GAN based on conditional GAN and U-Net structure, which can achieve supervised transformation from one image domain to another image domain, such as from sketch to color image, from day to night, etc. StarGAN [18] proposed a GAN based on conditional GAN and CycleGAN structure, which can achieve unsupervised transformation between multiple image domains, such as changing facial expressions, hairstyles, gender, etc. These methods provide some pioneering suggestions for solving this kind of problem.