Synthetic Data Augmentation

Synthetic data augmentation generates artificial data to supplement real-world datasets, primarily addressing limitations in data quantity, diversity, and balance crucial for training robust machine learning models. Current research focuses on using generative models like GANs, VAEs, and diffusion models to create realistic synthetic data for various applications, including medical image analysis, autonomous driving, and natural language processing. This technique significantly impacts fields with limited real data availability, improving model performance and generalizability while potentially reducing the cost and effort associated with data collection and annotation.