Diffusion Based Data Augmentation

Diffusion-based data augmentation leverages the power of generative diffusion models to create synthetic training data, addressing the limitations of small or imbalanced datasets in various machine learning tasks. Current research focuses on improving the fidelity and diversity of generated data, often incorporating techniques like conditional generation with text or image prompts, and integrating diffusion models with other architectures such as VAEs or LLMs to enhance control and semantic consistency. This approach holds significant promise for improving the performance and robustness of machine learning models across diverse applications, particularly in domains with limited labeled data, such as medical imaging and object detection.