Training Image

Training image research focuses on optimizing the use of images for training machine learning models, particularly in scenarios with limited data. Current efforts concentrate on improving data augmentation techniques using diffusion models and large language models to generate synthetic training images that maintain data distribution fidelity and enhance model robustness against attacks. This research is crucial for advancing various applications, including medical image analysis, object detection, and image generation, where acquiring sufficient labeled data is often challenging or expensive. The development of more efficient and effective training image strategies directly impacts the accuracy, generalizability, and reliability of machine learning models across numerous domains.