Large Scale Synthetic Dataset

Large-scale synthetic datasets are increasingly crucial for training and evaluating computer vision and natural language processing models, particularly in domains with limited or privacy-sensitive real-world data. Current research focuses on generating high-quality synthetic data for diverse applications, including medical text analysis, 3D object recognition, and autonomous driving, often employing techniques like diffusion models, large language models, and physically-based rendering to bridge the gap between synthetic and real-world data. These datasets are enabling advancements in model accuracy and robustness, particularly in handling challenging scenarios like occlusions and domain shifts, and are accelerating progress in various fields by providing readily available, high-quality training data.