Synthetic Image Data

Synthetic image data generation leverages machine learning to create realistic images for various applications, primarily addressing data scarcity issues in training AI models, particularly in medical imaging and other domains with limited real-world data. Current research focuses on improving the realism and diversity of synthetic images using techniques like diffusion models and generative adversarial networks (GANs), as well as developing methods to assess the fidelity of generated data and distinguish it from real images. This field is significant because it enables the development and evaluation of AI models in situations where acquiring sufficient real data is challenging or impossible, ultimately improving the accuracy and accessibility of AI-powered solutions across diverse scientific and practical applications.