Synthetic Training Image

Synthetic training images leverage generative models, like diffusion models and variational autoencoders, to create artificial data for training machine learning models, primarily addressing data scarcity and privacy concerns in various domains, including medical imaging and quality inspection. Current research focuses on improving the realism and diversity of synthetic images to minimize the performance gap between models trained on real versus synthetic data, often employing techniques like knowledge distillation and prompt engineering to enhance image quality and task relevance. This approach holds significant promise for advancing fields where acquiring large, labeled datasets is challenging or impossible, enabling the development of more robust and efficient AI systems across diverse applications.