Diffusion Based Augmentation

Diffusion-based augmentation leverages diffusion models to generate synthetic data, enhancing existing datasets for improved machine learning model performance. Current research focuses on applying this technique to address challenges like limited training data in specific domains (e.g., medical imaging, rare species identification), improving model robustness to variations and adversarial attacks, and facilitating efficient transfer learning in reinforcement learning. This approach offers a powerful tool for augmenting datasets without requiring extensive manual annotation, leading to more accurate and robust models across various applications.