Synthetic Augmentation

Synthetic augmentation leverages generative models to create artificial data, supplementing real-world datasets for training machine learning models, particularly in scenarios with limited labeled data. Current research focuses on improving the realism and utility of synthetic data using various architectures, including generative adversarial networks (GANs), latent diffusion models, and genetic algorithms, often tailored to specific application domains like medical imaging and LiDAR data processing. This approach addresses the critical need for large, high-quality datasets in deep learning, enhancing model performance and potentially reducing the cost and time associated with data acquisition and annotation in diverse fields.