Synthetic Training

Synthetic training leverages artificially generated data to train machine learning models, addressing data scarcity or high annotation costs in various domains. Current research focuses on generating high-quality synthetic data using techniques like text-to-image and text-to-audio models, coupled with model architectures such as transformers and diffusion models, and employing strategies like domain randomization and data augmentation to improve model generalization to real-world data. This approach is proving valuable for tasks ranging from object detection and pose estimation to natural language processing and medical image analysis, offering a powerful tool for advancing research and enabling practical applications where real data is limited or expensive to acquire.