Synthetic Target

Synthetic target generation is a rapidly growing field leveraging AI, particularly deep learning models like diffusion generative networks and GANs, to create artificial datasets for training machine learning algorithms. Research focuses on bridging the gap between synthetic and real-world data, employing techniques like data augmentation and adversarial training to improve model robustness and performance across diverse domains. This approach addresses limitations in real-world data availability, particularly in resource-constrained areas or specialized applications like drug discovery and disaster assessment, significantly impacting model accuracy and efficiency in various scientific and engineering disciplines.