generative models for defect synthesis

**Generative Models for Defect Synthesis** is the **use of generative AI (GANs, VAEs, diffusion models) to create realistic synthetic defect images** — augmenting limited real defect datasets to improve classifier training and address severe class imbalance. **Generative Approaches** - **GANs**: Conditional GANs generate defect images by type. StyleGAN for high-resolution synthesis. - **VAEs**: Variational autoencoders for controlled defect generation with interpretable latent space. - **Diffusion Models**: DDPM/stable diffusion for highest-quality defect image generation. - **Cut-Paste**: Synthetic insertion of generated defect patches onto normal background images. **Why It Matters** - **Class Imbalance**: Some defect types have <10 real examples — generative models create hundreds more. - **Privacy**: Synthetic data avoids sharing proprietary fab images with external ML teams. - **Rare Events**: Generate realistic samples of catastrophic but rare defects for robust training. **Generative Models** are **the defect image factory** — creating realistic synthetic defect data to augment limited real-world samples for better ML training.