Sample Generation

Sample generation, the creation of synthetic data points resembling a real-world dataset, aims to address data scarcity, improve model training efficiency, and enhance model understanding. Current research focuses on developing faster and more efficient generative models, such as diffusion models and flow-based methods, often incorporating techniques like classifier-free guidance and multisample couplings to improve sample quality and reduce computational cost. These advancements are impacting diverse fields, including computer vision, remote sensing, and time-series analysis, by enabling improved model training, data augmentation for rare classes, and the exploration of model decision-making processes.