## 2 Related Work
### Model-Based Image Augmentation  
Model-based Image Augmentation (MBIA) is an approach that leverages deep learning models to synthesize new data samples. With the advancement of deep learning, traditional data augmentation methods are gradually replaced by data augmentation algorithms based on deep learning frameworks. Deep learning models can learn latent feature distributions from raw data and can generate new data samples from random noise or conditional inputs. MBIA can effectively increase the size and diversity of datasets and can produce high-quality and high-fidelity data samples. [PERSON] et al. [35] proposed FeatMatch based on Convolutional Neural Networks (CNNs) [36], which replaces simple transformations in image space with complex transformations generated in feature space to achieve data augmentation effects in feature space, thus enhancing the data diversity and consistency. However, the lack of interpretability of vector data in feature space leads to difficult and time-consuming training. Therefore, [PERSON] et al. [37] changed the perspective of data augmentation to data space and found that data augmentation in data space is superior to data augmentation in feature space. However, both data augmentation methods in feature space and data space do not sufficiently learn the true distribution of the sample data, which makes the data augmentation methods based on Adversarial Generative Networks (GANs) [38] start to attract attention and research. GANs is a deep learning framework that consists of generative and discriminative models that compete with each other. GANs can learn the underlying data distribution from raw samples and generate novel samples with diverse attributes such as types, positions, sizes, and shapes. The generation and discrimination processes are driven by a zero-sum game that ensures the progressive convergence between the generated and authentic data distributions. However, the GAN training process faces many challenges due to its non-convex and non-cooperative nature. Mode collapse, gradient vanishing, and oscillatory disturbances are common problems that affect the quality and diversity preservation in generated samples. Various GAN variants such as WGAN [16], LS-GAN [39], and f-GAN [40] have introduced different loss functions and distance metrics to improve the similarity between generated and real samples. Likewise, models such as cGAN [14], AC-GAN [15], and InfoGAN [41] have modified the architectures of generators and discriminators to increase the expressiveness and diversity of generative models. However, due to the complex and variable features of industrial defects, relying only on GANs to generate new industrial defect samples from random noise might lead to significant differences or biases compared to real samples. The generated samples might lack plausibility or credibility, which limits the application of GANs in industrial defect sample generation. To endow GANs with more control mechanisms for sample generation, models such as Pix2 Pix [17] and CycleGAN [21] have used translation between different images to impose constraints on generated samples, ensuring their closer approximation to real images. Based on this idea, some researchers have explored industrial defect sample generation. [PERSON] et al. [42] proposed Tree-CycleGAN, a cyclic generative adversarial network based on a symmetric tree structure. This method uses a tree-structured generator with maximal diversity loss to enable one-to-many generation mappings. Using a tree-structured reconstructor and dual discriminators, Tree-CycleGAN can generate multiple target domain samples from a single source domain sample while preserving differences and cyclic consistency across different branches. This method effectively alleviates the problem of industrial defect sample insufficiency. Similarly, [PERSON] et al. [43] introduced CycleGAN-TSS, a Texture Self-Supervised CycleGAN that leverages texture information as a self-supervisory signal to guide the generator in acquiring enhanced shadow features.