Synthetic Data Generation Pipeline

Synthetic data generation pipelines are increasingly used to create training datasets for machine learning models, addressing limitations of real-world data such as scarcity, cost, privacy concerns, and class imbalance. Current research focuses on developing sophisticated pipelines that generate diverse and realistic synthetic data across various domains, including image processing, natural language processing, and 3D modeling, often leveraging techniques like generative adversarial networks (GANs) and neural radiance fields (NeRFs). These pipelines are proving valuable for improving model performance, particularly in scenarios with limited real-world data, and are accelerating progress in diverse fields by enabling more robust and efficient model training.