Synthetic Source Domain

Synthetic source domains are artificial datasets designed to train or evaluate machine learning models, particularly in scenarios where real-world data is scarce, expensive, or privacy-sensitive. Current research focuses on generating diverse and realistic synthetic data using techniques like diffusion models and adversarial learning, often incorporating detailed taxonomies to address specific challenges like safety evaluation in large language models or domain adaptation in object detection. These efforts aim to bridge the "sim2real" gap, improving model generalization and robustness by mitigating the discrepancies between synthetic training data and real-world deployment conditions, ultimately leading to more reliable and effective AI systems across various applications.